Claude Opus 5 vs Fable 5 vs GPT-5.6 Sol: Which Is Best?

The most useful way to compare Claude Opus 5 vs Fable 5 vs GPT-5.6 Sol is not to ask which model is universally smartest. It is to ask which one finishes *your* difficult work reliably, with the tools, latency, privacy terms, and total cost your application can accept.

All three are frontier models, but they were launched for different jobs:

• Claude Opus 5 is Anthropic's practical advanced model for complex agentic coding and enterprise work.
• Claude Fable 5 is Anthropic's maximum-capability model for the hardest, longest-running problems.
• GPT-5.6 Sol is OpenAI's frontier professional model with a particularly broad hosted tool and computer-use ecosystem.

This comparison uses public information available on July 27, 2026. Prices and availability can change, so verify the linked official documentation before making a production commitment.

The Short Answer

Claude Opus 5 is the best default for most advanced workloads. It offers a 1-million-token context window, 128,000-token maximum output, strong self-verification, and a $5 input / $25 output price per million tokens. It is the most balanced choice when quality, long context, and cost all matter.

Claude Fable 5 is the choice for maximum Anthropic capability. Use it when a hard, long-horizon task benefits enough from the extra capability to justify $10 input / $50 output pricing. Its mandatory 30-day retention requirement and safety fallback behavior also need review before regulated or model-sensitive deployments.

GPT-5.6 Sol is the strongest fit for tool-rich OpenAI workflows. It combines deep reasoning with web search, file search, code execution, hosted shell, computer use, MCP, image generation, and other Responses API tools. Its standard token price is close to Opus 5, but requests above 272,000 input tokens receive higher long-context pricing.

There is no honest universal winner. My practical recommendation is:

• Start with Opus 5 for difficult coding, research, and document work.
• Choose GPT-5.6 Sol when hosted tools, web interaction, computer use, or OpenAI's agent stack are central to the product.
• Escalate to Fable 5 only for the hardest tasks that demonstrate a measurable quality gain in your own evaluations.

Why This Is Not a Simple Benchmark Race

The public benchmark record does not contain a neutral, same-day, three-model test.

OpenAI's July 9 GPT-5.6 Sol launch analysis compares Sol directly with Fable 5 on several evaluations. Claude Opus 5 was released later, on July 24, so it is not in those charts. Anthropic's Opus 5 announcement uses a different set of evaluations, harnesses, effort settings, and cost measurements.

That means two statements can be true at once:

• OpenAI's published table shows Sol ahead of Fable 5 on some coding-agent and terminal tasks, while Fable leads on others.
• Anthropic says Opus 5 approaches Fable 5 on some agentic coding evaluations at roughly half the API price.

Neither statement proves that one model wins every workload. A benchmark score can change with the agent harness, available tools, reasoning effort, number of attempts, context supplied, and pass criteria. Use vendor benchmarks to decide what to test, not as a substitute for testing.

What Each Model Is Actually For

Claude Opus 5: The Practical Advanced Model

Anthropic positions claude-opus-5 as the recommended starting point for complex agentic coding and enterprise work. It has a 1M-token context window, up to 128K output tokens, adaptive thinking, vision, tool use, code execution, context editing, and stronger self-verification.

Opus 5 is designed for work that lasts long enough for planning discipline to matter:

• Multi-file debugging and codebase migrations
• Research that requires checking conflicting evidence
• Complex documents, spreadsheets, and presentations
• Browser or computer workflows that need visual judgment
• Long-running agents that must verify intermediate results
• Multi-agent coordination where one model reviews another's work

Its main advantage is balance. Opus 5 costs half as much per token as Fable 5 while keeping the same published context and output limits. Anthropic also gives it a May 2026 knowledge cutoff, newer than the cutoffs listed for the other two models.

For a deeper feature and migration breakdown, see my Claude Opus 5 developer guide.

Claude Fable 5: Maximum Capability, Maximum Commitment

claude-fable-5 is Anthropic's most capable widely released model. It targets the hardest reasoning, vision, debugging, research, and long-horizon agentic work.

Fable 5 supports a 1M-token context window, up to 128K output, adaptive thinking that cannot be disabled, task budgets, memory, code execution, programmatic tool calling, context editing, compaction, and vision. It is available through the Claude API and major cloud platforms.

The tradeoffs are substantial:

• Standard API tokens cost twice as much as Opus 5.
• Adaptive thinking is always enabled.
• Anthropic documents mandatory 30-day retention, so Fable 5 is not available under Zero Data Retention arrangements.
• Safety classifiers can decline a request or route it to a fallback when fallbacks are configured.
• The knowledge cutoff is January 2026, earlier than Opus 5's May 2026 cutoff.

Choose Fable when failure is more expensive than inference and your evaluations show that it completes a class of difficult tasks Opus cannot. For routine advanced work, Opus 5 is usually the more defensible starting point.

Read the Claude Fable 5 and Mythos 5 comparison for more context on Anthropic's model tiers.

GPT-5.6 Sol: Frontier Reasoning With a Broad Tool Stack

OpenAI describes gpt-5.6-sol as its frontier model for complex professional work. The gpt-5.6 alias currently routes to Sol. It has a 1,050,000-token context window, up to 128,000 output tokens, and a February 16, 2026 knowledge cutoff.

Sol's clearest differentiator is not a single benchmark. It is the breadth of the Responses API environment around the model:

• Web search and file search
• Image generation
• Code Interpreter and hosted shell
• Apply Patch and reusable skills
• Computer use
• MCP and tool search
• Function calling and structured outputs
• Programmatic Tool Calling
• Multi-agent orchestration in beta

Reasoning can be set to none, low, medium, high, xhigh, or max. OpenAI also offers a Pro reasoning mode through the API. In ChatGPT Work and Codex, Ultra is an application-level mode that coordinates four agents; it is not a separate Sol model.

Sol is a strong fit when the model must move beyond answering and operate across websites, files, terminals, applications, or external tool servers. The main pricing caveat is that inputs above 272K tokens move the entire request to a higher long-context rate.

Pricing and Context: The Clearest Difference

The list price per million tokens is:

Opus 5 and Fable 5 keep their published standard token rates across the 1M context window. For Sol, a prompt above 272K input tokens is billed at 2x the input rate and 1.5x the output rate for the entire request. OpenAI also documents a 1.25x charge for cache writes and a 90% discount for cache reads.

Example 1: A Large but Sub-272K Task

For 100K uncached input tokens and 10K output tokens, excluding tool charges:

• Claude Opus 5: $0.50 input + $0.25 output = $0.75
• Claude Fable 5: $1.00 input + $0.50 output = $1.50
• GPT-5.6 Sol: $0.50 input + $0.30 output = $0.80

At this size, Opus 5 and Sol are close. Fable costs approximately twice as much.

Example 2: A Very Long-Context Task

For 500K uncached input tokens and 20K output tokens, excluding tool charges:

• Claude Opus 5: $2.50 input + $0.50 output = $3.00
• Claude Fable 5: $5.00 input + $1.00 output = $6.00
• GPT-5.6 Sol: $5.00 input + $0.90 output = $5.90

Sol's request crosses the 272K threshold, so its effective rates become $10 per million input tokens and $45 per million output tokens. For consistently huge prompts, Opus 5 has a major price advantage.

These examples are rate-card calculations, not predictions of total task cost. Tokenizers, reasoning behavior, retries, cache hits, tool calls, and completion success can all change the real bill. The correct metric is cost per accepted result.

Coding and Agentic Work

OpenAI's July 9 comparison with Fable 5 shows a mixed result rather than a sweep:

• Sol leads the published Artificial Analysis Coding Agent Index, 80.0 to 77.2.
• Sol leads Terminal-Bench 2.1, 88.8 to 83.1.
• Fable leads SWE-Bench Pro, 80.0 to 64.6.
• Fable narrowly leads the published GDPval-AA v2 professional-work Elo, 1759.6 to 1747.8.
• Fable also narrowly leads the Artificial Analysis Intelligence Index, 59.9 to 58.9.

Those results suggest that agent harness and task type matter. Sol looks particularly strong in terminal and tool-mediated environments, while Fable can be stronger on some repository-level software tasks and professional-work evaluations.

Opus 5 arrived after that comparison. Anthropic reports that it finishes within 0.5% of Fable 5's peak score on CursorBench 3.2 at half the task cost, and highlights gains in long-horizon coding, self-verification, browser work, and office-document creation. Those are Anthropic's launch results, measured with Anthropic's methodology.

The most reliable coding evaluation is therefore your own:

1. Select 20 to 50 real issues from your codebase.
2. Give every model the same repository state, tools, instructions, and time limit.
3. Run each issue more than once to expose variance.
4. Score tests passed, regressions, review findings, latency, retries, and total cost.
5. Separate first-attempt quality from eventual success after correction.

A model that writes an impressive patch but misses the acceptance criteria should not beat a quieter model that closes the issue correctly.

Tools, Reasoning, and API Design

All three models can call tools and generate structured outputs, but their control surfaces differ.

Opus 5 uses adaptive thinking by default and exposes low, medium, high, xhigh, and max effort. Anthropic's API adds useful agent features such as mid-conversation tool changes, prompt caching, context editing, and server-side fallbacks. Web fetch is not currently available on Opus 5, and Priority Tier is not supported.

Fable 5 also uses adaptive thinking, but it cannot be disabled. It supports task budgets, memory, code execution, programmatic tool calling, context editing, compaction, and vision. Its higher price makes explicit routing and task budgets especially important.

GPT-5.6 Sol offers the broadest first-party hosted tool catalog of the three. Reasoning can be disabled for easy work or increased through six effort levels, with Pro mode for the deepest API reasoning. It supports streaming, structured outputs, and function calling, but not fine-tuning.

If your application already has mature internal tools, Fable or Opus may integrate perfectly well. If you want the model provider to supply web search, shell, computer use, file search, and orchestration primitives in one API, Sol has the clearest advantage.

Privacy, Retention, and Safety

Model quality is irrelevant if the deployment does not meet your data requirements.

Anthropic documents a mandatory 30-day retention period for Fable 5. Fable is therefore not available for Zero Data Retention use cases. Teams handling regulated, confidential, or contract-restricted data should treat that as a gating requirement, not a minor footnote.

Opus 5 does not carry Fable's documented mandatory-retention condition for general access, but actual retention depends on the product, contract, organization settings, and enabled features.

OpenAI documents Zero Data Retention compatibility for some API configurations and features, including Programmatic Tool Calling, but that does not make every Sol request automatically ZDR. Hosted tools can have their own data-handling behavior. Confirm the exact endpoint, feature set, account configuration, and contract.

Fable 5 also has a more visible safety-routing consideration. A request can finish with a refusal stop reason, and configured fallbacks can move a request to another model. Applications that require a specific model should log the model that actually completed each task.

For any provider, do not infer production privacy from the model name alone. Review current data controls and obtain the commitments your compliance process requires.

Which Model Should You Choose?

Choose Claude Opus 5 When

• You need the best default balance for difficult coding, research, and enterprise work.
• Your prompts regularly exceed 272K tokens and predictable long-context pricing matters.
• You want Fable-like capability on some tasks without paying Fable rates everywhere.
• Strong self-verification and document or visual work are important.
• A May 2026 knowledge cutoff is useful, while retrieval still supplies current facts.

Choose Claude Fable 5 When

• You are solving the hardest long-horizon tasks in Anthropic's ecosystem.
• A small improvement in completion quality is worth roughly twice Opus 5's token price.
• Your workload benefits from persistent adaptive thinking and task budgets.
• Mandatory 30-day retention is acceptable for the data involved.
• Your evaluation set demonstrates a real advantage over Opus 5.

Choose GPT-5.6 Sol When

• Web search, file search, hosted shell, computer use, MCP, or image generation are core parts of the workflow.
• Terminal-based coding agents and tool-heavy professional work dominate your evaluation set.
• You want reasoning control from none through max, or Pro mode for selected requests.
• Most prompts remain below 272K input tokens.
• Your team is already building on the Responses API and OpenAI agent ecosystem.

Use More Than One Model When

The best production architecture may be routing:

• Send routine extraction, formatting, and classification to a cheaper model.
• Use Opus 5 as the advanced default.
• Route tool-heavy OpenAI workflows to Sol.
• Escalate only the hardest unresolved Anthropic tasks to Fable 5.
• Ask a second model to review high-risk outputs instead of paying for the most expensive model on every request.

This approach treats models as infrastructure choices, not team loyalties.

Frequently Asked Questions

Is Claude Opus 5 better than Fable 5?

Fable 5 remains Anthropic's maximum-capability model, but Opus 5 is the better default for most advanced work because it costs half as much and approaches Fable on some Anthropic coding evaluations. Use your own hard tasks to determine whether Fable's additional capability justifies the price and retention constraints.

Is GPT-5.6 Sol better than Claude Opus 5?

Neither wins universally. Sol has a stronger hosted tool ecosystem and public results that look especially competitive for terminal and coding-agent work. Opus 5 has lower output pricing, standard-rate access across its full 1M context window, a newer documented knowledge cutoff, and strong positioning for self-verifying enterprise workflows.

Which model is best for coding?

Start with Opus 5 for complex everyday engineering, choose Sol for tool-rich terminal and OpenAI agent workflows, and test Fable 5 on the hardest long-horizon repository tasks. The published benchmarks use different harnesses, so a test suite made from your real issues is more predictive than one headline score.

Which model is cheapest?

For normal uncached API requests, Opus 5 is usually the least expensive of these three: $5 input and $25 output per million tokens. Sol matches the $5 input rate but charges $30 for output. Fable costs $10 input and $50 output. Sol can become nearly as expensive as Fable when a prompt crosses 272K input tokens.

Which model has the largest context window?

GPT-5.6 Sol lists a 1,050,000-token context window, while Opus 5 and Fable 5 list 1 million tokens. In practice, Opus has the most attractive published rate for prompts above 272K because Sol applies long-context multipliers and Fable costs twice Opus's standard token rate.

Which model is best for AI agents?

Opus 5 is the safest general recommendation for difficult agents, Fable 5 is the escalation model for maximum Anthropic capability, and Sol is strongest when the agent depends on OpenAI's hosted web, file, shell, computer-use, MCP, or orchestration tools.

Can Fable 5 be used with Zero Data Retention?

No. Anthropic documents a mandatory 30-day retention period for Fable 5 and says it is not available under Zero Data Retention arrangements.

Should I route tasks across all three models?

Yes, if the engineering complexity is justified by enough volume or task diversity. A simple starting policy is Opus 5 for difficult general work, Sol for OpenAI-hosted tool workflows, and Fable only after Opus fails or an evaluation predicts a meaningful quality gain.

Official Sources

• Anthropic: Introducing Claude Opus 5
• Anthropic: What's new in Claude Opus 5
• Anthropic: Introducing Claude Fable 5 and Mythos 5
• Anthropic: Claude model overview
• Anthropic: Claude API pricing
• OpenAI: GPT-5.6 launch
• OpenAI: GPT-5.6 Sol model documentation
• OpenAI: Latest model guidance

Final Comparison Table