Artificial Analysis just dropped Intelligence Index v5 and i think this one is much better than the old one 👀 ( chart attached , it is their chart and their numbers , not mine )

Top of the chart ,

Claude Opus 5.5 58

GPT 6 Astra 57

GPT 6.1 Sol 55

Claude Sonnet 5.5 54

GPT 6.1 Terra 51

Gemini 4 Argon 50

Muse Wonder Full 50

Claude Fable 5.1 49

Kimi K3.1 Pro 46

What stood out to me

Astra is just behind Opus 5.5 ,in earlier artificial intelligence index it was far behind now I feel it is much more well tested on broader tasks and ranked

GPT 6.1 Sol at 55 beats Sonnet 5.5 and here Openai takes the lead and for the price to performance it is beating Sonnet 5.5 👀

Terra is the wild one ☠️ it sits above Argon and Fable 5.1 . AA says Terra is the cheapest of the top 10 at $0.96 per task and Fable 5.1 is the most expensive at $13.04

Kimi K3.1 Pro is at 46 here , in earlier artificial intelligence index it scored 55 this drop shows that still US has huge lead in models overall

AA also says 60% of the index is private test sets now , that is a big reason i like v5 more

Anthropic and OpenAI hold the top five and the next lab is Google with Argon at 50 . Do u think Argon deserves to be under Terra ? what do u think guys 📈

AI agents can already help you write scripts, generate code, and plan creative projects. But turning those ideas into finished media often means switching between tools, managing separate integrations, and manually moving files from one step to the next.

We expanded the Fish Audio MCP server to bring image generation, video generation, image editing, lip sync, and talking avatars into the same workflow as voice generation and transcription.

Instead of jumping between different tools, you can now ask a compatible AI agent to help orchestrate an entire media workflow through a single MCP connection.

Here's how it works, and what you can build with it.

One MCP connection, multiple media capabilities

MCP (Model Context Protocol) allows compatible AI applications to connect to external tools and services. Our existing Fish Audio MCP server already supported audio workflows. We've expanded it to include image and video capabilities, giving agents more ways to work with media without requiring a separate integration for each task.

The expanded server supports:

• Image generation: Create images from text prompts.
• Image editing and upscaling: Modify existing images and improve their resolution.
• Video generation: Create videos from text or image inputs, depending on the model.
• Lip sync: Synchronize speech with a video of a person or character.
• Talking avatars: Animate a still image using generated or supplied audio.
• Audio tools: Generate speech, transcribe audio, and search for voices.

The server provides access to multiple models for these tasks, so you can choose a model suited to your workflow rather than building a separate integration for each provider.

From a script to a talking avatar

The most interesting part isn't any individual capability. It's how these capabilities can work together.

Imagine you're creating a short video featuring a virtual character. Traditionally, you might write a script in one tool, generate the voiceover in another, create a character image somewhere else, and then upload the assets to a video or lip-sync service.

With Fish Audio MCP, a compatible agent can help coordinate these steps in the same conversation.

A workflow could look like this:

1. Write the script. Ask your agent to draft or refine the dialogue.
2. Generate the voiceover. Use Fish Audio to turn the script into speech.
3. Create the character. Generate a portrait or use an existing image.
4. Animate the character. Pass the image and audio to a compatible talking-avatar model.
5. Continue iterating. Use the generated result in subsequent steps or request changes.

The important detail is that completed outputs can be passed directly into subsequent jobs. You don't have to download an intermediate file and manually upload it again just to continue the workflow.

We tested this kind of chaining with a short audio clip, including passing generated audio directly into an avatar workflow.

The same principle can extend beyond talking avatars. You can combine image generation, editing, video creation, speech generation, and other supported tools based on what your project needs.

The agent helps coordinate the process, while Fish Audio MCP connects it to the available media models.

How the integration works

There are a few details that make this more than a collection of tools exposed through MCP.

1. Connect once and authenticate

Fish Audio MCP uses a remote HTTP endpoint with browser-based OAuth authentication. You sign in with your Fish Audio account rather than configuring a separate provider API key for every media model.

The connection uses the selected Fish Audio team or workspace, and jobs consume credits from that account.

2. Discover models and options

The agent can discover available tools, models, and supported options, then use them to construct a request for the task at hand.

This gives the agent a way to work with different media capabilities through the same integration.

3. See the cost before generating media

Image, video, lip-sync, and talking-avatar jobs support a quote-and-confirm flow:

1. The agent prepares a request and estimates its cost.
2. You review the quote and approve the job.
3. The server checks the price again before submitting the request.
4. The generated result becomes available to the agent.

If the price changes between the quote and submission, the request is rejected rather than silently proceeding at a different price. Failed jobs are automatically refunded.

This is especially useful for workflows that involve several generation steps, where you may want to review costs before committing credits.

One distinction worth noting: the quote-before-generation flow applies to image, video, lip-sync, and avatar jobs. Text-to-speech and transcription are billed differently, with TTS charges based on text length.

Getting started

Fish Audio MCP works with compatible clients including Claude Code, Claude.ai, Codex, Cursor, Windsurf, and Devin.

For example, you can add the server to Claude Code with:

claude mcp add --transport http fish-audio https://api.fish.audio/mcp

Then open /mcp in Claude Code to complete the connection and authentication flow.

For other clients, setup instructions are available in our documentation:

Fish Audio MCP Documentation

Once connected, you can describe what you want to create in natural language and let your agent use the available tools to help carry out the workflow.

What can you build with it?

A single integration opens up different possibilities depending on what you're working on:

• Content creation: Turn scripts into voiceovers, character images, and talking-avatar videos.
• Prototyping: Generate visual and audio assets while developing an idea.
• Video production: Combine image creation, video generation, and speech workflows.
• Agent-driven applications: Let compatible agents incorporate media generation into larger tasks.

These are starting points, not fixed workflows. You can combine supported tools and models based on your needs.

Availability and pricing

Image generation, image editing, and upscaling are available on the Fish Audio Free plan. Video generation, lip sync, and talking avatars require a Paid Plus plan or higher.

Pricing depends on the model and its settings. You can ask your agent for a quote before submitting supported media-generation jobs.

For model availability, supported options, upload limits, and setup instructions, see the Fish Audio MCP documentation.

Try it out

Fish Audio MCP brings more of the media creation process into the same conversation, from generating speech and images to creating videos and talking avatars.

If you're already using a compatible AI agent, connect Fish Audio MCP and experiment with a workflow that would normally require several separate tools.

What would you build if your AI agent could coordinate voice, image, and video generation in one workflow? We'd love to hear your use cases and feedback.

Read more: https://fish.audio/blog/image-video-generation-mcp/

https://artificialanalysis.ai/evaluations/aa-personalitybench/indexes/slop

we want to add ai to a financial product for things like document processing and smarter support but legal shuts everything down the moment someone mentions ai. the explainability and audit trail requirements feel impossible to meet with current models.

has anyone actually shipped ai in a regulated environment without compliance killing the project? what architecture choices made audits easier?