GPT-5.5 vs Claude Fable 5 vs Gemini 3.1 Pro

What You'll Learn

• What OpenAI and Anthropic officially published about GPT-5.5 and Claude Fable 5.
• Why benchmark scores from different labs are not a single objective league table.
• How access status, context limits, safeguards, pricing, and model variants affect a real choice.
• How to match a model to coding, research, document, tool-use, and cost-sensitive workloads.

What This Comparison Can and Cannot Prove

A model comparison can report documented capabilities, published evaluation results, price pages, access routes, and test conditions. It cannot prove that one model is best for every prompt, team, language, workflow, or risk profile. The original article used phrases such as undisputed champion and clear winner without enough qualification. Those phrases are removed.

OpenAI's GPT-5.5 page and Anthropic's Fable 5 page are vendor announcements. Their benchmark tables, examples, and capability descriptions are useful primary evidence, but they are not an independent tournament. The models can be run with different prompts, reasoning settings, tool access, context, and evaluation harnesses.

The comparison also has a date boundary. The evidence in this article was checked on August 23, 2026. Model names, access plans, API prices, and safety controls can change after publication. A later model release does not make the original title false, but it can make a ranking stale.

GPT-5.5: OpenAI's Published Position

OpenAI's GPT-5.5 announcement is dated April 23, 2026. The page says GPT-5.5 is designed for coding, online research, data analysis, document creation, software operation, and multi-step work with tools. OpenAI describes these as model capabilities and says it evaluated the model under its safety and preparedness processes.

The page says GPT-5.5 was rolling out to ChatGPT and Codex plans. It also contains an April 24 update about GPT-5.5 and GPT-5.5 Pro API availability. Because the page includes both launch language and a later availability update, the article does not compress all access conditions into one permanent statement.

OpenAI reports Terminal-Bench 2.0 at 82.7%, GDPval wins or ties at 84.9%, OSWorld-Verified at 78.7%, BrowseComp at 84.4%, and SWE-Bench Pro at 58.6% for GPT-5.5 under the listed conditions. These numbers belong to OpenAI's published evaluation and should be read with the stated harness, model variant, and prompt conditions.

OpenAI also says that some evaluations were run with reasoning effort set to xhigh and in a research environment. That caveat matters when a reader compares a vendor test with a production chat experience. A public benchmark is evidence of performance on that test, not a guarantee about an ordinary user's entire workflow.

Claude Fable 5: Anthropic's Published Position

Anthropic's Claude Fable 5 announcement is dated June 9, 2026. It describes Fable 5 as a Mythos-class model made available for general use and discusses software engineering, knowledge work, vision, scientific research, and safety controls.

The Anthropic page includes important status history. An update says Fable 5 and Mythos 5 were redeployed on July 1, 2026. The page also records that access was suspended on June 12 while Anthropic worked to restore service. That history does not mean the model is unusable. It means availability should be described with a dated source and not as an uninterrupted guarantee.

Anthropic says Fable 5 is offered at $10 per million input tokens and $50 per million output tokens on the cited announcement. That is an Anthropic-stated price for the stated offering. It should not be compared with another provider's price without checking whether the products use the same context tier, batch mode, cache treatment, output allowance, or access channel.

Anthropic also describes safeguards that can cause some topics to receive a response from Claude Opus 4.8 instead. This is a product and safety behavior, not a benchmark failure. A team choosing Fable 5 should test the exact prompts and policy-sensitive tasks it expects to run.

Gemini 3.1 Pro in the Published Record

The original article presented Gemini 3.1 Pro as an undisputed reasoning champion and claimed a two-million-token context window. The official material fetched for this repair does not support using those statements as universal facts. The safest treatment is to keep Gemini 3.1 Pro as a comparison reference where OpenAI's table lists it, while avoiding invented or unverified Google scores.

Google's official Gemini Developer API pricing page is the relevant primary source for current Gemini API pricing. The price depends on model, token tier, and service mode. A third-party summary or a stale screenshot should not replace Google's live price table.

OpenAI's published comparison table includes Gemini 3.1 Pro in selected rows, including Terminal-Bench 2.0 at 68.5%, GDPval wins or ties at 67.3%, Toolathlon at 48.8%, BrowseComp at 85.9%, and FrontierMath figures in the listed evaluation table. These are values shown in OpenAI's comparison, not an independent Google benchmark page. The article therefore labels them as cross-vendor figures reported by OpenAI.

A fair reader should also ask whether the Gemini model was run with the same tool access, reasoning settings, context, and prompt format. Without that information, the scores can inform a shortlist but cannot settle the choice.

Benchmark Results Need Their Labels

Benchmarks are measurements, not a single definition of intelligence. Terminal-Bench 2.0 measures command-line workflows. OSWorld-Verified measures computer operation. BrowseComp measures research and browsing tasks. SWE-Bench Pro evaluates software issue resolution. GDPval evaluates specified knowledge work across occupations.

Even when two models appear in one table, the evaluation may be run by one vendor and may use different availability, prompt, or tool conditions. OpenAI's GPT-5.5 page explicitly identifies some internal evaluations and states conditions for others. That is why this article writes OpenAI reports instead of the stronger claim the model objectively wins.

Coding: Choose the Workflow, Not the Slogan

GPT-5.5 is a strong candidate for long coding tasks that require planning, tool use, validation, and changes across a codebase. OpenAI reports gains on Terminal-Bench 2.0 and SWE-Bench Pro and describes behavior such as holding context, checking assumptions, and carrying work through surrounding files.

Anthropic describes Fable 5 as strong in software engineering and says its early testing included large codebase work. That is meaningful vendor evidence, but a team should still test repository conventions, review quality, patch safety, test generation, and the model's response to ambiguous requirements.

The right evaluation is a controlled sample of the team's real tasks. Measure accepted pull requests, human correction time, test failures, security findings, token cost, and time to recovery after a failed attempt. A score from a public benchmark is one input, not the final engineering decision.

Our AI coding agents guide provides adjacent context. It should not be read as a substitute for testing a model on the reader's own repository.

Agentic Tasks and Computer Use

Agentic performance involves more than producing a correct paragraph. The system must interpret intent, decide what to do next, call tools, inspect results, recover from errors, and stop at the right time. OpenAI positions GPT-5.5 for this type of work and reports scores for Terminal-Bench 2.0, OSWorld-Verified, BrowseComp, Toolathlon, and related evaluations.

Anthropic's Fable 5 page also emphasizes autonomy and longer tasks. The model may be attractive where a team wants extended reasoning and document or code work, but safety routing and access conditions can affect the actual experience.

Agent tests should include permissions, secrets handling, browser failures, rate limits, ambiguous instructions, and human approval points. A model that scores well in a clean benchmark can still need a narrow tool policy in production.

For a practical multi-model perspective, see our AI model pricing comparison. Prices and access should be checked again before procurement.

Reasoning, Research, and Documents

Research quality depends on evidence retrieval, source evaluation, calculations, uncertainty, and revision. GPT-5.5's OpenAI page reports results across knowledge work, academic, and scientific evaluations. Anthropic's Fable 5 page describes document, vision, and scientific research capabilities. Those descriptions support testing these workflows, but they do not prove that either provider is best for every discipline.

For research teams, compare citation accuracy, refusal behavior, long-document extraction, table interpretation, calculation reliability, and the rate of unsupported claims. For document teams, compare formatting fidelity, spreadsheet formulas, change tracking, and the quality of review comments.

Do not treat an attractive benchmark as a substitute for source-grounded review. A model may score well on an abstract task and still require retrieval tools, a second pass, or a human sign-off in a regulated workflow.

Our AI agent career and skills guide covers adjacent work implications. It does not establish model performance or employment outcomes.

Context Windows and Long Documents

Context window figures are easy to misread. The original article claimed a two-million-token Gemini context and presented context size as a direct proxy for capability. That claim is removed because the official sources used here do not establish it in the required form.

OpenAI's GPT-5.5 page states a 400K context window in Codex and describes a 1M context window for the planned API offering. The article reports those values with their product context instead of treating them as one universal number.

Even a large context does not guarantee perfect recall. Long-document tests should measure retrieval of facts at different positions, conflicting instructions, tables, citations, and the model's ability to say when information is missing. Teams should also consider latency and cost as the context grows.

Pricing: Compare the Same Unit

Anthropic's cited announcement states $10 per million input tokens and $50 per million output tokens for Fable 5 and Mythos 5. OpenAI's GPT-5.5 page states planned API pricing of $5 per million input tokens and $30 per million output tokens, with separate GPT-5.5 Pro pricing and product availability language. Google directs developers to its official Gemini Developer API pricing page for current Gemini tiers.

These figures should not be read as a simple claim that one model is cheaper. A total bill also includes output length, cached input, batch discounts, priority processing, tool calls, retries, context tier, subscription fees, and human review. A lower token rate can cost more if a workflow needs more retries or produces more output.

Run a representative cost test with the same prompts and output requirement. Record successful task cost, not just listed token price. Include failed calls, review time, tool charges, and the cost of switching models when a safeguard or access restriction changes the route. For a company-cost example outside model procurement, compare our Amazon Stock 2026 analysis, which also separates reported facts from forward claims.

Availability, Safety, and Data Handling

Access is part of model performance. OpenAI's GPT-5.5 page describes ChatGPT and Codex rollout and includes API timing updates. Anthropic's Fable 5 page records both a June access suspension update and a July redeployment update. These dated notices show why a procurement decision should verify current access rather than rely on an old comparison article.

Safety behavior can also alter output. Anthropic says some topics may receive a response from Claude Opus 4.8 under its Fable 5 safeguards. OpenAI describes safeguards, preparedness evaluations, and access controls for GPT-5.5. A team should test policy-sensitive work through the actual account and deployment route it plans to use.

Data retention, training controls, regional availability, administrative settings, and contractual terms are separate questions. This article does not make a privacy or compliance determination for a business. The relevant provider terms and enterprise agreements must be reviewed directly.

A Practical Selection Matrix

Use a selection matrix rather than a universal ranking. Start with the task, then set minimum quality, latency, cost, safety, and availability requirements. If two models pass, use a small production pilot and measure the outcome that matters to the team.

For another technology comparison, our AI engineer and H-1B guide shows why a current process guide should distinguish official facts from assumptions. The same discipline applies to model comparisons.

Conclusion: There Is No Verified Universal Winner

OpenAI's published GPT-5.5 evidence supports testing it for agentic coding, computer use, knowledge work, and research. Anthropic's Fable 5 announcement supports testing it for software engineering, knowledge work, vision, and scientific work, with important access and safeguard caveats. Google's official pricing page is the correct source for current Gemini API price checks, while the original article's unverified universal reasoning and context claims are removed.

The result is not a single winner. A defensible choice depends on the team's prompts, tools, risk controls, data, latency target, access route, and successful task cost. Re-run the comparison when a provider changes a model, price, context limit, or safety policy.

This article is for information only and is not a recommendation to purchase, deploy, or rely on any model for a particular business, legal, medical, financial, or security decision.