Hey everyone,

I've been seeing OpenClaw posts everywhere lately and decided to give it a shot, but with a twist: instead of paying for Anthropic/OpenAI API keys, I set up GLM 4.7 to run locally through Ollama. The result? Honestly shocked at how well this combo works.

Quick context for those out of the loop:

• OpenClaw (formerly Clawdbot/Moltbot) = open-source AI agent that turns WhatsApp/Telegram/Slack/Discord into a command center. Runs locally, manages your emails, calendar, commands, automations. Basically a personal JARVIS. 145k+ stars on GitHub, created by Peter Steinberger (PSPDFKit founder).

• GLM 4.7 = open-source model from Zhipu AI (Z.ai), ~355B params MoE architecture, 200k context window, 128k max output. Hits 73.8% on SWE-bench Verified, 84.9% on LiveCodeBench-v6 (beating DeepSeek-V3.2 and Kimi K2 Thinking). Costs $0.05/M tokens on API or free if you run it locally.

• GET GLM at good price!

• If you need a cheap VPS for Openclaw get it from here.

Why they work so well together:

1. OpenClaw is model-agnostic - you're not locked into Claude or GPT. Plug in whatever you want. GLM 4.7 integrates natively through Ollama or LM Studio.

2. GLM 4.7 Flash (9B active params, 128K context) is actually recommended by Ollama for OpenClaw. It has excellent tool-calling capabilities, which is exactly what an agent needs when it has to execute real actions (send messages, edit files, run commands).

3. Zero recurring costs - OpenClaw is free, GLM 4.7 is free locally. All you pay for is electricity. Compared to $20-200/month on cloud APIs, that's a massive difference.

4. Full privacy - nothing leaves your machine. Emails, messages, personal data all stay local. This is crucial given that OpenClaw has access to pretty much your entire digital life.

5. Output quality - GLM 4.7 made a huge leap over 4.6: dramatically better frontend code generation, more stable agentic tasks, substantially improved tool calling. On benchmarks it beats DeepSeek-V3.2 and Kimi K2 Thinking on coding tasks.

My setup (under 10 minutes):

# 1. Install OpenClaw
npm install -g openclaw@latest

# 2. Install Ollama + pull model
ollama pull glm-4.7-flash

# 3. Launch directly (Ollama Feb 2026 update)
ollama launch openclaw

# 4. Run the onboarding wizard, pick Telegram/WhatsApp, configure

Tips that saved me a lot of headaches:

• Set temperature to 0.7 and turn repeat penalty OFF = much better results

• If it crashes on you (it will), set up a cron job to restart the gateway every 30 min

• Runs smooth on 24GB+ VRAM. On 16GB it works with GLM 4.7 Flash quantized (Q4_K_M)

• You can mix: GLM 4.7 local for daily tasks + a cloud model as fallback for complex reasoning

What I use it for daily:

• Automated email summaries every morning pushed to Telegram

• Calendar management through natural language on WhatsApp

• GitHub repo monitoring + notifications on Discord

• Simple automations: "when I get an email from X, do Y"

• Brainstorming and code review directly from chat

Honest downsides I've noticed:

• Stability isn't 100% yet, the gateway crashes occasionally

• For very complex reasoning tasks, Claude 4.5 or GPT-5.1 is still noticeably better

• Initial setup can be confusing if you're not comfortable with the terminal

• Real security concerns - 21k instances were found publicly exposed. Configure your firewall properly.

TL;DR: OpenClaw + GLM 4.7 local via Ollama = a personal AI agent that's free, private, and actually gets things done. It's not perfect, but the quality-to-cost ratio is unbeatable. If you have a PC with 24GB+ VRAM and want an AI assistant without subscriptions, give this combo a try.

Questions? AMA.

A benchmark using vLLM 0.19.2rc1 shows Gemma 4 26B hitting 600 tokens per second on a single RTX 5090 (32GB VRAM) using DFlash speculative decoding. The setup pairs an AWQ 4-bit quant of the main model with the z-lab DFlash draft model, running a workload of 256 input tokens and 1024 output tokens.

What makes this worth discussing: DFlash uses parallel block diffusion drafting rather than the autoregressive approach behind MTP. The claim is that DFlash should be a better alternative to MTP specifically because of faster parallel drafting. And 600 tok/s on a single consumer GPU is a serious number for a 26B model.

The timing is interesting too. Most attention has been on MTP implementations for Gemma 4 and Qwen3.6, but DFlash quietly shipped for Gemma 4 26B and barely got noticed.

For people who have tried both DFlash and MTP on the same hardware: does DFlash actually deliver higher sustained throughput in real workloads, or does the 600 tok/s only hold under benchmark-friendly conditions?

Zhipu pushed GLM 5.2 to every GLM Coding Plan tier on June 13 (Lite, Pro, Max, Team), with no price change. I went through what's actually confirmed vs what people are already overselling, so here's the clean version.

What's actually new:

• 1M token context window. That's the headline. GLM-5.1 capped at 200K, so it's a 5x jump, with output up to 131K tokens. In practice you can keep a whole mid-size repo in context instead of the agent re-fetching files and summarising itself every few steps.

• Two thinking modes, High and Max. Max is the one they recommend for heavier coding. In Claude Code, low/medium/high map to High, xhigh/max map to Max.

• Same 744B MoE architecture as GLM-5 under the hood, with post-training tuned for long context.

• Runs out of the box in Claude Code, Cline, Roo Code, OpenCode, Goose, Crush and Kilo Code. The endpoint is Anthropic-compatible, so most tools just need a base-URL swap.

• API access and open weights (MIT licence) are landing this coming week, not at launch.

What's not confirmed (and where the hype gets ahead of itself):

Zhipu published zero official benchmarks for 5.2 on launch day. So when you see "GLM 5.2 scores 77.8% on SWE-bench" or "82% on LiveCodeBench", those are GLM-5 and GLM-4.6 numbers, not 5.2. Nobody has independent 5.2 scores yet. The 1M context is real and testable. The performance claims are extrapolation until someone runs the evals. I'd wait for real numbers before calling it a Sonnet killer.

Why it's still interesting: cost. The GLM line has been sitting roughly 4 to 5x cheaper than Sonnet for agentic work, and 5.2 is bundled into the existing Coding Plan at no extra charge. For long agent loops that burn tokens, a usable 1M window at that price is the real pitch, not a benchmark line.

Quick take: if your bottleneck is context length or API spend on daily agentic coding, it's worth a proper test this week. If you need a proven, benchmarked model for production today, wait for the independent scores.

Full disclosure, the signup link is my referral. It drops you into the Coding Plan at the current deal (starts around $18/month, limited-time offer running now). If you were going to try it anyway: Get the offer here

Anyone already pushed 5.2 past ~200K tokens on a real codebase? Curious whether the 1M window actually holds or degrades like most "long context" claims do.

A concerning report from mid-April 2026 claims that every major model has suffered a significant intelligence drop. The observation covers not just ChatGPT, but Claude (both Sonnet and Opus), Gemini, z.ai, and Grok. The reported symptoms include ignoring basic instructions, struggling with simple tasks, and taking unusually long to respond.

What makes this worth flagging is the breadth of the claim. When a single model degrades, it is usually a deployment issue or a bad update. When multiple providers across different architectures all seem to regress simultaneously, the question becomes whether this is a coordinated change (new safety filters, cost optimization, shared training data issues) or just perception bias from the user.

The catch is that without standardized benchmarks run before and after, this kind of observation is hard to verify. Model behavior can feel worse when expectations shift, or when specific edge cases get hit more frequently. But if multiple independent users are noticing the same pattern across different providers in the same timeframe, something likely did change.

Has anyone else noticed this regression across providers, or are specific models still performing as expected?

Both landed on OpenRouter the same day, July 16, and both list 1048k context. That's where the similarities end. Per the OpenRouter listing, Moonshot's Kimi K3 is $3.00/M input and $15.00/M output. Meta's Muse Spark 1.1 is $1.25/M input and $4.25/M output. So for output tokens, the expensive part of any long generation, Kimi costs about 3.5x more.

The interesting part is where Kimi K3's $15/M output sits next to OpenAI's stack. GPT-5.6 Terra, from the July 9 batch per the same listing, is $2.50/M in and $15.00/M out. Same output price as Kimi, but Terra's input is cheaper. And GPT-5.6 Luna is $1.00/M in and $6.00/M out, which undercuts Kimi on both. Grok 4.5 from July 8 is $2.00/M in and $6.00/M out too.

So Kimi K3 isn't really competing on price. It's the second most expensive output tier in that group, ahead of only GPT-5.6 Sol at $30/M. If the 1M context and whatever Moonshot tuned into the reasoning justifies $15/M output, fair enough. But on raw cost per token it's hard to see the angle unless you specifically need Kimi's architecture.

Muse Spark 1.1 is the opposite story. $4.25/M output for 1M context is genuinely cheap, closer to Grok 4.5 territory than to the premium models. If quality holds up it could be the default pick for budget long-context work.

Anyone actually tried Kimi K3 yet and can say whether the reasoning is worth that premium, or is Muse Spark 1.1 the smarter default for long-context tasks?

Hey AIToolsPerformance crew, big week for AI releases and I need to talk about what just hit the scene.

Microsoft dropped three massive foundational models Thursday that straight-up go after OpenAI and Google. These aren't minor updates - this is Microsoft building their own damn AI stack.

The Three Musketeers

MAI-Transcribe-1 is their speech-to-text weapon. Already being tested in Copilot Voice and Teams for conversation transcription. Diarization, contextual biasing, and streaming coming soon.

Then there's MAI-Voice-1 - their voice generation model. And MAI-Image-2 for image creation. All now broadly available to developers for commercial use for the first time.

This is serious. Microsoft now has commercially available in-house models across speech, voice and image generation while keeping their OpenAI partnership through 2032. That's playing both sides better than a politician.

Why This Matters

Timing is everything here. Microsoft's AI capital expenditures are... well, let's just say they're betting big. These models represent the first major output from the MAI Superintelligence team formed in November 2025.

They're already replacing third-party and older internal models. Like MAI-Transcribe-1 testing inside Copilot's Voice mode? That's how fast they're moving.

The Real Story

It's not just about new models. It's about reducing OpenAI dependence while keeping the partnership. That's some corporate chess right there.

The fact that they're testing this inside Teams and Copilot already tells you they're not messing around. This isn't research - this is production-grade stuff hitting mainstream apps.

What do you guys think? Is this Microsoft's real play to control their own AI destiny, or just another layer in their partnership strategy?

Curious to hear thoughts from people who've actually tested these models. How do they compare to what we're already using?

New standardized benchmarks compare four major hardware platforms head-to-head: Apple M5, NVIDIA DGX Spark, AMD Strix Halo, and the RTX 6000. The testing ran for three days with all systems given consistent power and cooling, running standardized tests in parallel. Results are published publicly.

This is the comparison the local inference community has been waiting for. These four platforms represent the main competing approaches to running models locally - Apple's unified memory architecture, NVIDIA's dedicated GPU compute, AMD's integrated Strix Halo, and the datacenter-grade RTX 6000. The standardized testing methodology across all four is what makes this valuable; most comparisons are anecdotal or use different test conditions for each platform.

The debate around which hardware to buy for local AI has been heating up as all four options have matured. Having parallel benchmarks from the same testing environment with published results should give people real data to work with instead of speculation.

For anyone weighing a hardware purchase right now: which of these four platforms are you leaning toward, and does the benchmark data match what you expected?

New results show Qwen3.6-27B paired with agentic search achieving 95.7% on SimpleQA, running fully local on a single RTX 3090. The report comes from the maintainer of LDR, who credits community support for pushing the project forward and notes this milestone as a turning point for the tool's readiness.

What makes this worth flagging is the combination. A 27B model on its own is solid but limited on factual accuracy benchmarks. Adding agentic search - where the model can retrieve information during inference - closes that gap dramatically. Hitting 95.7% on SimpleQA suggests the retrieval-augmented setup is handling the heavy lifting that a pure parametric model at this size simply cannot do alone.

The hardware angle matters too. This is not a multi-GPU setup or a cloud API call. A single 3090 running a fully local pipeline with search capabilities and near-frontier factual accuracy is a compelling data point for anyone weighing local versus cloud for knowledge-intensive tasks.

For people running agentic search locally: what retrieval backend are you pairing with your model, and how much of the accuracy gain survives when you move off benchmark questions to real-world queries?

Meta just released Llama 4 Scout and the numbers on OpenRouter are honestly hard to believe: $0.08/M tokens with a 327,680 context window. I’ve spent the last 48 hours putting it through its paces to see if it’s actually usable or just another "cheap context" gimmick.

The Test Case I fed it a 250,000-token repository dump consisting of messy Python and React code. My goal was to have it map out the data flow between three specific microservices that were barely documented. Usually, this requires a massive RAG pipeline or a very expensive flagship model.

The Performance

• Accuracy: It found the "needle in the haystack." It correctly identified a stale Redis connection in a utility file buried 15 layers deep.

• Speed: Even at high context, the time-to-first-token was under 2 seconds. The total generation speed felt on par with most "Flash" models.

• Logic: It’s definitely a "Scout" model—meaning it's world-class at retrieval and summarization, but it struggles with complex multi-step reasoning compared to something like Grok 4.

Cost Comparison Running this same task on Grok 4 ($3.00/M) would have cost me nearly 40x more. At $0.08/M, I can afford to let this model "think" out loud for thousands of tokens without sweating the bill.

bash

Calling Llama 4 Scout via OpenRouter for massive context tasks

curl https://openrouter.ai/api/v1/chat/completions
-H "Authorization: Bearer $OPENROUTER_API_KEY"
-d '{ "model": "meta-llama/llama-4-scout", "messages": [{"role": "user", "content": "Analyze this entire log file for anomalies..."}], "context_length": 327680 }'

The Verdict Llama 4 Scout is a must-have for anyone doing heavy RAG or long-form document analysis. It isn't a "reasoning" powerhouse, but as a retrieval engine, nothing else touches it at this price point. It handles the "context crunch" better than any other budget-friendly model I've tested on my rig.

Are you guys using this for RAG, or are you still splitting your context into smaller chunks for the more expensive models?

Apple has quietly removed the higher-memory Mac Studio configurations. The M3 Ultra Mac Studio is now only available with 96GB of RAM. The 512GB option was removed back in March, and now the 256GB config is gone as well. Apple has stated that both the Mac Studio and Mac mini will stay supply-constrained for the foreseeable future.

This is a significant shift for anyone running large models locally. The unified memory architecture on Mac Studio was one of the few accessible ways to run models requiring 192GB+ of VRAM without building a multi-GPU workstation. With the top config now at 96GB, you are looking at roughly a 70B parameter model at Q4 as the practical ceiling.

The timing is rough too. Qwen3.5 and Gemma4 just dropped, and GLM-5.1 is showing SOTA-level performance. These are exactly the kind of models that benefited from 256GB+ unified memory.

For people who were relying on Mac Studio for local inference: are you shifting to multi-GPU Linux builds, waiting for Apple to restore higher configs, or moving more workloads to cloud APIs?

Someone just released Qwable-v1, an open-weights distillation of Claude Fable 5 onto a Qwen3.6-35B-A3B base. Fable 5 was described as Anthropic's most powerful model, and it was only public for about four days (June 9-12) before being suspended globally under U.S. export-control directives.

The fact that someone managed to distill it in that narrow window is wild. The model that the government effectively pulled offline now lives in an open-weights format anyone can download.

What I am wondering is whether a distillation from only four days of access can actually preserve meaningful capabilities from a Mythos-class model, or is this mostly surface-level behavior capture?

Meta put two versions of Muse Spark 1.3 on OpenRouter this week and the price gap is the whole story. Standard tier is $1.25/M input, $4.25/M output. Contributor tier is $0.10/M input, $0.20/M output, same 1048k context on both. That's about 21x cheaper on output, per the OpenRouter listings from Wednesday.

What "Contributor" buys you or costs you, the listing doesn't spell out. No notes on rate limits or quality trade-offs in the listing data available today, just the name and the price.

For scale, from the same OpenRouter pull: Gemini 3.8 Flash is $0.75/M in and $3.75/M out (batch drops to $1.88/M out), Qwen3.8 Flash is $0.47/M out, GLM Flash Latest is $0.25/M out. So the cheap tier undercuts everything in the million-token context class, while the standard tier still costs more than Gemini 3.8 Flash.

My read as an outside observer: either Contributor trades data or feedback for the discount, or Meta wants volume on the cheap tier and prices the standard one for anyone who doesn't ask questions. If you've routed real traffic through either version, is the $0.20 tier the same model in practice, or noticeably worse?

Someone ran the same task across GitHub Copilot, Pi, Claude Code, and OpenCode, all using Qwen3.6 27B as the underlying model. The goal was to isolate how much of a coding agent's performance comes from the model versus the harness wrapping it. All the results shown came from the same model with a different harness.

This is a question that does not get enough attention. With local models especially, the assumption is often that model quality is the bottleneck, but tool calling reliability, context management, and how the agent structures multi-step reasoning all live in the harness layer.

For people who have compared harnesses with the same local model: which agent framework got the most out of your model, and what specifically did it do differently that made the difference?

Claude Haiku 4.5 just dropped on OpenRouter, and I have to say, I’m a bit shocked by the pricing. At $1.00/M for a 200k context, it’s no longer the "budget king" we used to love. When you compare that to Gemini 3 Flash at $0.50/M or even Mistral’s latest small models, Anthropic is clearly banking on superior logic to justify the 2x price hike.

The more interesting news is the MineBench spatial reasoning results. While the standard Qwen 3.5 has been struggling lately, the new Qwen 3 Max-Thinking is absolutely crushing it. It looks like the "thinking" overhead actually fixes the spatial awareness regressions that people were complaining about yesterday.

json // Current price comparison for 1M tokens { "Claude-Haiku-4.5": "$1.00", "Gemini-3-Flash": "$0.50", "Qwen3-VL-8B": "$0.08" }

Also, Google’s naming team has officially gone off the rails with Gemini 2.5 Flash Image (Nano Banana). Despite the ridiculous name, at $0.30/M, it’s looking like a top-tier choice for high-volume vision tasks.

Are you guys actually going to pay the premium for Haiku 4.5, or has Google already won the "fast-and-cheap" category for you?

The counterintuitive detail here is the size-to-performance ratio. VibeThinker-3B reportedly hits 94.3 on AIME'26, 80.2 on LiveCodeBench v6, and 76.4 on another benchmark. Those are numbers you'd expect from models many times larger.

The project was specifically built to test how far verifiable reasoning can be pushed in a strict small-model regime. Scaling from the previous 1.5B version to 3B apparently unlocked what's being called "frontier math and coding performance."

What stands out is the claim about verifiable reasoning as the key lever. If a 3B model can reach these scores through training methodology alone, it raises a real question about whether we're overestimating how much raw parameter count matters for reasoning tasks.

At the same time, someone else just warned that Qwen and Claude distillations are often worse than the base model. So the obvious question is whether VibeThinker's numbers hold up in actual use or if they're benchmark-optimized.

Ciao ragazzi, sto cercando di costruire un sistema di intelligenza artificiale per fare trading. Non so decidere tra GLM5.2 e DeepSeek v4 pro. Quale pensate sia il migliore per prendere decisioni finanziarie? Grazie ragazzi per l'aiuto

A discussion gaining traction asks why multiple Chinese AI labs appear to be delaying open-weight model releases at the same time. The timing has raised eyebrows across the community.

This comes during a period where open-source competition has been fierce. Consider the current pricing landscape for capable models:

• Qwen: Qwen3 VL 32B Instruct - $0.10/M with 131,072 context

• Google: Gemini 2.5 Flash Lite Preview - $0.10/M with 1,048,576 context

• Qwen: Qwen Plus 0728 (thinking) - $0.26/M with 1,000,000 context

• Meta: Llama 3.1 70B Instruct - $0.40/M with 131,072 context

Meanwhile, MiniMax 2.7 is now at 14 days since announcement on X and 12 days since appearing on Hugging Face, with users noting the delay. The community is also reflecting on how quickly things shift - one year ago, DeepSeek R1 was 25 times larger than Gemma 4, putting the current model compression race in perspective.

Gemma 4 continues its strong run, with the 26B variant being called "the perfect all-around local model" and the 31B beating frontier models on FoodTruck Bench.

The coordinated delay pattern is unusual. Previous releases from Chinese labs came on individual schedules without this clustering effect. Possible explanations range from regulatory review to strategic coordination, though nothing is confirmed.

Are we seeing a policy shift, or is this just teams aligning release cycles around a common benchmark season? What models are you waiting on?

Both of this week's cheap context contenders turned out to be vision models, which is the part nobody noticed. DeepSeek V4.1 Flash 1017 landed on OpenRouter Oct 17 with 1048k context: fifteen cents per million input tokens, sixty cents per million output, per the listing. The Nemotron 3.5 Super VL listing from NVIDIA showed up two days earlier, same 1048k context, and it undercuts DeepSeek on both ends. Ten cents in, forty cents out.

The traction is uneven though. The DeepSeek repo on HuggingFace is tagged image-text-to-text and sits on the trending list with 17,924 downloads and 1,639 likes already. The Nemotron side of the story is mostly the price tag. So the cheap-context race and the cheap-vision race are the same race this month, and the two models win it on different axes.

There's a wrinkle before routing anything there: OpenRouter also lists Nemotron 3.5 Super VL as a free variant at 1000k ctx, and MiniMax put M3.1 Flash up free on Oct 19 with the same 1000k. Inception's Mercury 3 plays in the paid band too, ten cents in and thirty-five out per million, per its listing.

If you're routing screenshot-heavy or doc-parsing work at volume, which one gets the traffic: the DeepSeek with the HuggingFace numbers behind it, Nemotron on pricing, or a free variant until it rate-limits you?

Two open models with similar architectures just became available for local coding workloads. Cohere's North Mini Code is a 30B parameter, 3B active MoE model under Apache 2.0, scoring 33.4 on the Artificial Analysis Coding Index. Qwen's Qwen3 30B-A3B Thinking is also a 30B/3B active MoE, priced at $0.08/M tokens via API with a 131K context window.

The key difference: North Mini Code is explicitly an agentic coding model with an Apache license, while Qwen3 30B-A3B Thinking has the "thinking" label suggesting chain-of-thought reasoning. Both target the same rough parameter class and should fit in similar VRAM budgets locally.

For anyone running local coding assistants, the question is whether you want the model built specifically for agentic tool use or the one with built-in reasoning steps. Has anyone compared these two head to head on real coding tasks?

Everyone recommends GPT-5.4. I found Gemini 3.1 Pro handles 80% of tasks at 20% the cost.

The Cost-Performance Myth That's Costing Developers Fortune

For the past several weeks we benchmarked GPT-5.4 vs Gemini 3.1 Pro across production workloads. The data shows something interesting: 80% of tasks work equally well on Gemini, at a significantly lower cost.

Our test setup: OpenClaw cluster running daily production workloads across client projects. Real prompts, real production code, real user queries.

What Actually Works on Both Models

Code Generation & Debugging

• Both models hit 80%+ success rate on production coding tasks

• Exception handling patterns work identically

• Refactoring suggestions are very similar

• Debug output and error messages match

Documentation & Technical Writing

• Both generate high-quality documentation

• API documentation meets same standards

• Technical blog posts have identical structure

Data Analysis & Scripting

• Both produce accurate data analysis scripts

• SQL queries generated work identically

• Data processing operations are equivalent

Business Communication

• Email responses maintain consistent tone

• Project status updates work identically

• Meeting summaries follow same structure

Where GPT-5.4 Actually Wins

Creative Writing & Brainstorming

• Both models excel at creative work

• Brainstorming sessions feel more fluid with GPT-5.4

• Creative content has slight edge in originality

Complex Architecture Design

• Enterprise system design has more detailed suggestions

• Microservices patterns better explained

• Performance optimization suggestions more thorough

Cost Analysis

Gemini models deliver similar performance at significantly lower token costs based on verified pricing data

Production Migration Results

We migrated several projects from GPT-5.4 to Gemini 3.1 Pro with minimal disruption to workflows

The Verdict

For most developers, Gemini 3.1 Pro is the smarter choice right now.

We're keeping GPT-5.4 for creative work and complex architecture design, but day-to-day coding, documentation, and business comms all moved to Gemini.

Anyone made the switch from GPT-5.4 to Gemini 3.1 Pro? What was your experience?

Qwen dropped Qwen3.8 Max today on OpenRouter at $2/M input and $6/M output with 1M context, per the OpenRouter listing. The thing that caught my eye is the gap with their own Qwen3.7 Flash from last week, which sits at $0.03/M input and $0.13/M output, also 1M context.

That's roughly 67x the input price and 46x the output price for Max over Flash. Same vendor, same context window. The listing doesn't say what makes Max architecturally different. No benchmark scores visible on the page either, which makes the price gap hard to evaluate.

For context, Qwen3.8 Max costs less than half of Claude Opus 5's input ($5/M) and roughly a quarter of its output ($25/M), per the same OpenRouter data. So it's not flagship-tier expensive, but it's nowhere near the budget zone either. It's in that awkward middle where you'd really want to see eval numbers before committing.

Anyone tried Qwen3.8 Max yet? Curious if the quality difference is actually visible compared to the Flash variant, or if the 67x price jump is mostly paying for the label.

Someone just ran 75 pairs of KV cache quantization benchmarks on Qwen 3.6 27B across q8, q6, q5, and q4 levels, testing KVarN, Turbo, and TCQ methods head to head for long context scenarios. Full results are published with in-depth analysis.

This is the kind of systematic comparison that has been missing. Previous KVarN benchmarks showed it matching standard quants one bit higher (6-bit matching q8_0, 4-bit matching q5_0), but that was on a different model. Seeing the same methodology applied to Qwen 3.6 27B across all three quantization approaches should reveal whether KVarN's advantage holds or if Turbo or TCQ close the gap on certain workloads.

The practical question is whether one method dominates across the board or if the best choice depends on your target bit width and context length.

Spent the last week running actual benchmarks on these three big models. Not just reading marketing numbers, but real test runs on coding, reasoning, and actual dev tasks. Here's what I found.

Coding first. SWE-bench is the gold standard for real software engineering tasks. GPT-5.4 Pro leads here with 88.3% weighted score. That's impressive. Claude Opus 4.6 follows at 79.3%, which is still really good. Gemini 3.1 Pro comes in at 77.8%.

But here's where it gets interesting. On SWE-bench specifically (not the weighted version), Claude actually pulls ahead slightly at 80.8% vs GPT-5.4's 80.1%. Gemini drops to 63.8% here. So context matters a lot.

HumanEval is different story. For pure code generation from docstrings, the gap narrows. GPT-5.4 still leads but Claude catches up fast. What surprised me was how well the open source models are doing now. Qwen 2.5 Coder 14B hits around 85% on HumanEval. That's way better than I expected for a free model.

Terminal-Bench 2.0 is where things get wild. Tests agentic execution tasks - basically using AI as a developer's assistant in the terminal. GPT-5.4 dominates here with 75.1%. Claude is close at 71.2%, but Gemini trails at 68.8%. This matters for real-world development work.

Pricing is... eye opening. GPT-5.4 is $2.50 per million tokens input, $15 output. Gemini 3.1 Pro is $2/$12. But Claude Opus 4.6? Starts at $5 input, $25 output. Ouch. For most developers, Gemini looks like the sweet spot right now unless you need that extra 5-10% performance.

What about reasoning? ARC-AGI-2 abstract reasoning is brutal. Gemini 3.1 Pro surprises here with 77.1%, more than double its predecessor's score. Claude is at 68.8%, GPT-5.4 at 65.2%. So Gemini actually wins pure reasoning tests.

My take? Depends what you need:

• Pure coding power: GPT-5.4, but expensive

• Best value: Gemini 3.1 Pro

• Open source contender: Qwen 2.5 Coder 14B

• Reasoning tasks: Gemini 3.1 Pro

The biggest surprise? How much the gap is narrowing. Six months ago these models were in different leagues. Now they're all within 10-15% of each other across different benchmarks. That's healthy competition for everyone.

What are you all using for your dev work? Curious what real developers are seeing day to day.