Claude Fable 5 Benchmark vs Gemini 3.1, GPT-5.5 and Grok 4

Claude Fable 5 launched on June 9, 2026, with a strong focus on autonomous coding, computer use, and complex professional workflows. For developers, the relevant question is not whether the model sets a new headline score, but whether those results translate into fewer failed tool calls, better repository-level changes, and less human supervision. 

This Claude Fable 5 benchmark comparison evaluates where it leads against GPT-5.5, Gemini 3.1 Pro, and Grok 4, while also highlighting the workloads where competing models remain stronger.

What Is Claude Fable 5?

Claude Fable 5 is Anthropic’s first Mythos-class model, released on June 9, 2026. It sits above the previous Claude Opus 4.8 tier and is designed for workloads that require sustained autonomy, including repository-scale coding, computer use, and long-running agentic tasks.

The main difference from Opus 4.8 is not simply higher response quality. Fable 5 is built to maintain context and execute multi-step work across much larger environments, with a context window exceeding one million tokens. For API users, this means fewer manual handoffs when analyzing large codebases, coordinating tools, or completing tasks that span many files and dependencies.

A concrete example comes from Stripe, which used Fable 5 to complete a migration across a 50-million-line codebase in one day. The same project would normally have required a development team around two months, showing how the model’s autonomy can translate into shorter execution cycles for large engineering projects.

Key specs at a glance

• Model tier: Anthropic Mythos-class
• Model ID: claude-fable-5
• Context window: 1M+ tokens
• API pricing: $10 per million input tokens and $50 per million output tokens
• Availability: Claude API, Amazon Bedrock, and GitHub Copilot

For teams already using Opus 4.8, Fable 5 is most relevant when the bottleneck is not generating code, but completing complex workflows reliably with less human intervention.

Coding Performance: SWE-Bench Pro

Claude Fable 5 scores 80.3% on SWE-Bench Pro, giving it the strongest coding result among the four models compared. Grok 4 is the closest competitor at approximately 75%, while GPT-5.5 reaches 58.6% and Gemini 3.1 Pro scores 54.2%.

Fable 5’s lead suggests it is better suited to longer, multi-step engineering tasks where the model must navigate dependencies and maintain context across several actions, rather than only produce short code snippets.

Stripe’s reported 50-million-line migration provides a practical connection to this result. Fable 5 completed in one day a task that would normally take a team around two months, indicating that its coding performance can extend beyond controlled benchmark environments.

However, SWE-Bench Pro focuses on Python repositories. Teams working primarily with other languages should validate Fable 5 against their own codebases before treating this lead as universal.

Reasoning & Knowledge Work

Claude Fable 5 does not lead every reasoning benchmark. On GPQA Diamond, which evaluates difficult graduate-level science questions, Gemini 3.1 Pro ranks first at 94.3%, followed by GPT-5.5 at 92.8% and Fable 5 at 91.3%.

For scientific reasoning workloads, Gemini 3.1 Pro still has a slight edge. Teams building applications around advanced scientific question answering should not assume that Fable 5’s coding lead also makes it the strongest model for every knowledge-intensive task.

The result changes when the workload becomes more representative of enterprise analysis. Claude Fable 5 ranks #1 across all models on Hebbia’s Finance Benchmark, which covers complex, document-heavy tasks such as chart interpretation, reasoning across multiple documents, and structured problem solving. Exact numerical scores are not available, so the result should be treated as a ranking rather than a direct score comparison.

The practical distinction is workload type. Fable 5 may underperform Gemini on narrow PhD-level science questions, but it appears stronger on analytical workflows that require extracting evidence, connecting information across long documents, and producing a structured conclusion. For finance, consulting, due diligence, and research automation, that broader reasoning profile may matter more than a small GPQA difference.

The Legal Agent Benchmark measures whether a model can complete multi-step legal reasoning tasks autonomously, including document review, case analysis, and structured output generation. Absolute scores remain low across all models because this is a difficult, emerging benchmark, not because the systems are unusable for legal work.

The gap between Fable 5 and Gemini 3.1 Pro, 13.3% versus 0.0%, is relevant for teams building legal research, contract review, or compliance automation workflows, but it should be treated as directional while the benchmark continues to mature.

Vision & Computer Use

Claude Fable 5 scores 85.0% on OSWorld-Verified, a benchmark that tests whether an AI can operate a computer by navigating interfaces, clicking controls, and completing multi-step tasks across applications. This measures more than visual recognition. The model must interpret what appears on screen, choose the correct action, and recover as the interface changes.

Fable 5’s vision capabilities also extend to extracting precise numerical values from scientific figures and reconstructing web application source code from screenshots alone. These abilities are relevant when visual information must be converted into structured data or executable output.

Fable 5’s 85.0% score is strong, but it should not be treated as proof that it outperforms those models on computer use.

For developers, this matters most in agentic workflows, visual document processing, and automated QA testing. Fable 5 can potentially inspect interfaces, execute actions, extract information, and validate application behavior within the same workflow.

Which Model Should You Use?

Pricing Comparison: Claude Fable 5 vs Gemini 3.1, GPT-5.5 and Grok 4

Claude Fable 5 is the most expensive model in this comparison at $10 per million input tokens and $50 per million output tokens. Its pricing is easier to justify for coding agents, large codebase analysis, and computer-use workflows where higher reliability can reduce retries, failed executions, and human review time.

Gemini 3.1 Pro offers the lowest API pricing at $2 input and $12 output, while also leading GPQA Diamond. This makes it the strongest cost-performance option for scientific reasoning, document analysis, and high-volume workloads that do not require Fable 5’s coding advantage.

Grok 4 sits between Gemini and GPT-5.5 on price. At $3 input and $15 output, combined with an approximately 75% SWE-Bench Pro score, it may provide the best balance for cost-sensitive coding workloads.

GPT-5.5 costs $5 input and $30 output, but trails both Fable 5 and Grok 4 on SWE-Bench Pro. Its value will therefore depend more on workload fit, ecosystem requirements, and production testing than on coding benchmark performance alone.

The context window also affects total cost. Fable 5, GPT-5.5, and Gemini 3.1 Pro support around one million tokens, while Grok 4 is limited to 256K tokens, which may require splitting very large documents or codebases across multiple requests.

Access Claude Fable 5, Gemini 3.1, GPT-5.5, and Grok 4 in one API

Eden AI gives developers access to Claude Fable 5, GPT-5.5, Gemini 3.1 Pro, Grok 4, and hundreds of other models through one unified REST API. You can switch providers by changing the model parameter, without maintaining separate integrations, provider accounts, or API keys.

import os
import requests

MODELS = [
    "anthropic/claude-fable-5",
    "google/gemini-3.1-pro-preview",
    "openai/gpt-5.5",
    "xai/grok-4",
]

PROMPT = "Hello world !. Can you tell me a joke ?"

for model in MODELS:
    response = requests.post(
        "https://api.edenai.run/v3/chat/completions",
        headers={
            "Authorization": f"Bearer {os.environ['EDENAI_API_KEY']}",
            "Content-Type": "application/json",
        },
        json={
            "model": model,
            "messages": [
                {
                    "role": "user",
                    "content": PROMPT,
                }
            ],
        },
        timeout=60,
    )

    print(f"\n{'=' * 60}\nMODEL: {model}\n{'=' * 60}")
    try:
        response.raise_for_status()
        print(response.json()["choices"][0]["message"]["content"])
    except requests.HTTPError as e:
        print(f"Error: {e}\n{response.text}")

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Eden AI is particularly useful for multi-model benchmarking because you can:

• Compare models on your own prompts and data, rather than relying only on published benchmark scores.
• Configure automatic fallback routing when a selected model or provider is unavailable.
• Manage one invoice and one API key across all supported providers.

FAQs - Claude Fable 5 Benchmark