GPT-5.6 Sol vs Claude Fable 5: features, performance, benchmarks, pricing, and real differences
- 2 days ago
- 12 min read
GPT-5.6 Sol and Claude Fable 5 are two of the clearest frontier-model rivals for users who care about advanced reasoning, long-running agents, coding, knowledge work, document-heavy workflows, and high-value professional tasks.
OpenAI positions GPT-5.6 Sol as the flagship tier of the GPT-5.6 family, with Terra and Luna serving lower-cost or faster use cases, while Anthropic describes Claude Fable 5 as its most capable widely released model for demanding reasoning and long-horizon agentic work.
The comparison is strong because both models target difficult work rather than ordinary short chat, although they reach that goal through different product strategies.
OpenAI’s advantage is the broader GPT-5.6 family, where Sol, Terra, and Luna let users route work by capability, speed, and cost, while Anthropic’s advantage is the concentration of its top capability into Fable 5, a model built for long autonomous tasks, large context, tool use, vision, and high-end enterprise workflows.
The pricing difference is immediately visible, because GPT-5.6 Sol is listed at $5 input and $30 output per million tokens, while Claude Fable 5 is listed at $10 input and $50 output per million tokens.
The benchmark picture is more complex than the pricing picture, because GPT-5.6 Sol leads or looks highly competitive in several agentic and workflow evaluations, while Claude Fable 5 still shows major strength in areas such as SWE-Bench Pro, AA-Briefcase, analytical quality, and long-horizon agentic work.
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GPT-5.6 SOL AND CLAUDE FABLE 5 ARE BOTH FRONTIER MODELS FOR HARD PROFESSIONAL WORK.
The comparison is strongest when the focus is coding, agents, long workflows, document-heavy analysis, and complex reasoning rather than everyday chatbot use.
GPT-5.6 Sol is the flagship model in OpenAI’s GPT-5.6 family, and OpenAI presents the broader GPT-5.6 release as a system designed to deliver more useful work from each token, stronger performance per dollar, and more capability on demand for difficult tasks.
Claude Fable 5 is Anthropic’s most capable widely released model, with an API model ID of claude-fable-5, and Anthropic describes it as built for the most demanding reasoning and long-horizon agentic work.
That makes the comparison more meaningful than pairing GPT-5.6 Sol against a mid-tier Claude model, because both sides are aimed at users who need frontier-level performance for complex workflows rather than simple conversational answers.
Claude Fable 5 also supports a 1M token context window and up to 128k output tokens per request, while GPT-5.6 Sol sits inside a broader OpenAI stack that includes ChatGPT, ChatGPT Work, Codex, and API access across Sol, Terra, and Luna.
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· GPT-5.6 Sol is OpenAI’s flagship GPT-5.6 tier.
· Claude Fable 5 is Anthropic’s most capable widely released model.
· Both models target demanding reasoning and agentic workflows.
· The comparison is strongest for professional work, not casual chat.
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Positioning in one view
Area | GPT-5.6 Sol | Claude Fable 5 |
Provider | OpenAI | Anthropic |
Model role | Flagship GPT-5.6 tier | Most capable widely released Claude model |
Main target | Complex work, coding, agents, knowledge work | Demanding reasoning and long-horizon agentic work |
Product strategy | Family with Sol, Terra, Luna | Single top widely released model |
Best comparison angle | Cost-efficient frontier reasoning | Long-running agentic capability |
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THE PRICING ADVANTAGE IS CLEARLY ON GPT-5.6 SOL’S SIDE.
GPT-5.6 Sol costs less per token than Claude Fable 5, which makes OpenAI’s model easier to justify when the workload is large, repeated, or output-heavy.
OpenAI lists GPT-5.6 Sol at $5 per million input tokens and $30 per million output tokens, while Anthropic lists Claude Fable 5 at $10 per million input tokens and $50 per million output tokens.
That means GPT-5.6 Sol is half the input price of Claude Fable 5 and 40% cheaper on output tokens, before considering cache behavior, reasoning effort, tool calls, retries, and task-level efficiency.
The difference becomes especially important for developers, agent builders, coding tools, knowledge-work platforms, and companies that process long documents or run many iterative tasks, because small per-token differences can become large monthly infrastructure differences.
Claude Fable 5 can still be worth the higher cost when its long-horizon planning, context handling, refusal behavior, safety posture, or stronger performance on a specific benchmark is more valuable than token savings.
The cleanest pricing conclusion is that GPT-5.6 Sol starts with a substantial cost advantage, while Claude Fable 5 has to justify its premium through model behavior, workflow reliability, and superior results in the tasks where it performs best.
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· GPT-5.6 Sol is cheaper on input tokens.
· GPT-5.6 Sol is also cheaper on output tokens.
· Claude Fable 5 carries a premium price.
· The real cost depends on retries, reasoning effort, caching, and task completion quality.
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API pricing comparison
Model | Input price / 1M tokens | Output price / 1M tokens |
GPT-5.6 Sol | $5.00 | $30.00 |
Claude Fable 5 | $10.00 | $50.00 |
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GPT-5.6 SOL LOOKS ESPECIALLY STRONG ON COST-PER-TASK AND CODING-AGENT EFFICIENCY.
Independent Artificial Analysis data presents GPT-5.6 Sol as very close to Claude Fable 5 in broad intelligence while costing much less per task.
Artificial Analysis reports that GPT-5.6 Sol max scores one point below Claude Fable 5 max on its Intelligence Index, while costing approximately one third as much per task.
The same analysis says GPT-5.6 Sol leads the Artificial Analysis Coding Agent Index at 80 points, with lower cost per task than Claude Fable 5 max and Claude Opus 4.8 max in their respective coding-agent harnesses.
That matters because the most useful comparison is often not the absolute score alone, but the relationship between score, latency, token use, and task completion cost.
A model that is slightly behind on one broad intelligence index can still become the better production choice if it reaches similar quality at much lower cost and with strong agentic-coding performance.
Claude Fable 5 remains extremely competitive in the same Artificial Analysis ecosystem, because the same source says Fable 5 max still leads AA-Briefcase, largely due to stronger rubric score and analytical quality.
The result is a split picture: GPT-5.6 Sol has a strong efficiency story, while Claude Fable 5 still has a strong quality story in some realistic knowledge-work evaluations.
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· GPT-5.6 Sol comes very close to Claude Fable 5 in the Artificial Analysis Intelligence Index.
· GPT-5.6 Sol is reported at roughly one third of Claude Fable 5’s cost per task in that index.
· GPT-5.6 Sol leads the Artificial Analysis Coding Agent Index.
· Claude Fable 5 still leads AA-Briefcase in the same Artificial Analysis report.
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Artificial Analysis comparison signals
Area | GPT-5.6 Sol | Claude Fable 5 | Reading |
Intelligence Index | 1 point below Fable 5 | Higher by 1 point | Very close broad score |
Cost per Intelligence Index task | About one third of Fable 5 | Higher | GPT-5.6 Sol cost advantage |
Coding Agent Index | 80 | 77.2 | GPT-5.6 Sol lead |
AA-Briefcase | Second to Fable 5 | Leads | Claude Fable 5 knowledge-work lead |
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OPENAI’S OWN BENCHMARKS SHOW A MIXED CODING PICTURE RATHER THAN A SIMPLE GPT-5.6 WIN.
GPT-5.6 Sol leads on several agentic and terminal-style coding tests, while Claude Fable 5 has a major advantage on SWE-Bench Pro.
OpenAI’s published table reports GPT-5.6 Sol at 80 on the Artificial Analysis Coding Agent Index v1.1, compared with 77.2 for Claude Fable 5.
The same OpenAI table gives GPT-5.6 Sol 72.7% on DeepSWE v1.1 compared with 69.7% for Claude Fable 5, and 88.8% on Terminal-Bench 2.1 compared with 83.1% for Claude Fable 5.
Claude Fable 5, however, is listed at 80% on SWE-Bench Pro, while GPT-5.6 Sol is listed at 64.6%, which is a large counterweight to any claim that GPT-5.6 Sol simply dominates coding.
This is the most important benchmark nuance in the comparison.
GPT-5.6 Sol looks stronger on agentic coding, terminal-style work, and some workflow-heavy evaluations, while Claude Fable 5 remains very strong when the benchmark is SWE-Bench Pro.
For a developer, that means the better model depends on whether the task resembles long terminal execution, code-agent orchestration, repository repair, benchmark-style issue resolution, or a broader coding assistant workflow.
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· GPT-5.6 Sol leads on Coding Agent Index, DeepSWE, and Terminal-Bench 2.1.
· Claude Fable 5 leads strongly on SWE-Bench Pro.
· Coding performance should be separated by benchmark type.
· The better coding model depends on the actual workflow.
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Selected coding benchmarks
Benchmark | GPT-5.6 Sol | Claude Fable 5 | Lead |
Artificial Analysis Coding Agent Index v1.1 | 80 | 77.2 | GPT-5.6 Sol |
SWE-Bench Pro | 64.6% | 80% | Claude Fable 5 |
DeepSWE v1.1 | 72.7% | 69.7% | GPT-5.6 Sol |
Terminal-Bench 2.1 | 88.8% | 83.1% | GPT-5.6 Sol |
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LONG-RUNNING AGENTS ARE WHERE CLAUDE FABLE 5 HAS ITS CLEAREST IDENTITY.
Anthropic built Fable 5 around demanding reasoning and long-horizon agentic work, which makes it especially relevant for workflows that need sustained attention across many steps.
Anthropic describes Claude Fable 5 as its most capable widely released model for demanding reasoning and long-horizon agentic work, and the model supports effort control, task budgets, memory, code execution, programmatic tool calling, compaction, and vision.
That tool and context profile gives Claude Fable 5 a clear role in large autonomous workflows, where the model may need to process large source material, carry state across a long task, use tools, and produce a result that can be reviewed by a human team.
GPT-5.6 Sol also targets complex work and agentic execution, with OpenAI emphasizing tool coordination, multi-agent beta support, programmatic tool calling, explicit cache behavior, and API access across Sol, Terra, and Luna.
The difference is one of emphasis.
Claude Fable 5 is positioned as Anthropic’s top general-release model for long-horizon agents, while GPT-5.6 Sol is positioned as the flagship of a broader family designed to scale capability and cost across different surfaces.
For teams building agentic systems, Claude Fable 5 may feel more naturally aligned with sustained autonomous work, while GPT-5.6 Sol may offer a stronger balance of agentic capability and cost efficiency.
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KNOWLEDGE WORK AND DOCUMENT TASKS ARE CLOSE, WITH DIFFERENT STRENGTHS.
GPT-5.6 Sol has strong presentation and artifact-generation signals, while Claude Fable 5 remains very strong in analytical quality and structured project work.
Artificial Analysis reports that GPT-5.6 Sol max ranks second only to Claude Fable 5 max in AA-Briefcase, while also having the highest Presentation Elo of any model in that benchmark.
The same source says Claude Fable 5 leads AA-Briefcase largely because of a stronger rubric score and higher Analytical Quality Elo, which gives Claude a meaningful advantage in some realistic knowledge-work evaluations.
OpenAI’s own benchmark table shows a narrow split in GDPval-AA v2, with GPT-5.6 Sol at 1,747.8 Elo and Claude Fable 5 at 1,759.6 Elo, which suggests both models are very strong on economically valuable work rather than producing a decisive single-model victory.
GPT-5.6 Sol may be especially compelling when the task includes presentation quality, artifact polish, coding-agent integration, and cost efficiency.
Claude Fable 5 may be more compelling when analytical quality, rubric adherence, long-context reasoning, and structured project execution carry more weight than price.
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· GPT-5.6 Sol has very strong presentation-quality signals.
· Claude Fable 5 leads AA-Briefcase overall in the Artificial Analysis report.
· GDPval-AA v2 is close between the two models.
· The knowledge-work winner depends on whether visual polish or analytical quality matters more.
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Knowledge-work signals
Benchmark or signal | GPT-5.6 Sol | Claude Fable 5 | Reading |
AA-Briefcase | Second | First | Claude Fable 5 overall lead |
Presentation Elo | Highest recorded | Lower than GPT-5.6 Sol | GPT-5.6 Sol presentation lead |
Analytical Quality Elo | 1592 | 1764 | Claude Fable 5 analytical lead |
GDPval-AA v2 | 1,747.8 Elo | 1,759.6 Elo | Very close, slight Claude lead |
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GPT-5.6 SOL HAS A STRONGER PRICE-PER-PERFORMANCE STORY, WHILE CLAUDE FABLE 5 HAS A STRONGER PREMIUM-MODEL STORY.
The most important business difference is that GPT-5.6 Sol can look close or ahead in several areas while being cheaper, whereas Claude Fable 5 asks users to pay more for Anthropic’s strongest available capability.
GPT-5.6 Sol’s pricing makes it easier to deploy at scale, especially when tasks generate long outputs or require repeated attempts across many users.
Claude Fable 5’s pricing makes more sense when the workflow benefits enough from Anthropic’s long-context, agentic, and analytical strengths to justify the premium.
Artificial Analysis gives GPT-5.6 Sol a particularly strong case by reporting similar broad intelligence to Claude Fable 5 at about one third of the cost per task, while OpenAI’s own pricing table puts Sol below Fable 5 on both input and output token prices.
Claude Fable 5 still has a clear premium case, because it remains Anthropic’s most capable widely released model and leads or looks stronger in important areas such as AA-Briefcase, analytical quality, SWE-Bench Pro, and some long-context or reasoning-heavy workflows.
For production teams, the practical question is whether the Claude premium produces fewer failures, better final artifacts, stronger adherence to the task, or better long-horizon continuity in the specific workflow being automated.
If the answer is yes, Fable 5 can justify the higher price.
If the answer is no, GPT-5.6 Sol becomes hard to ignore because its cost structure is much more favorable.
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SAFETY AND REFUSAL BEHAVIOR ARE MORE VISIBLE IN CLAUDE FABLE 5’S API DESIGN.
Claude Fable 5 includes safety classifiers that can decline requests, and Anthropic exposes refusal handling as a specific integration concern.
Anthropic says Claude Fable 5 includes safety classifiers that can decline certain requests, while Claude Mythos 5 shares Fable 5’s capabilities without those classifiers and is available only in limited release through Project Glasswing.
When Claude Fable 5 declines a request, the Messages API returns a successful HTTP 200 response with a refusal stop reason rather than treating the refusal as a technical error.
Anthropic also documents fallback paths, including server-side, client-side, and manual retry approaches, so developers integrating Claude Fable 5 need to treat refusals as part of normal application design rather than as rare edge cases.
OpenAI’s GPT-5.6 material emphasizes capability, cost efficiency, tool calling, multi-agent support, prompt caching, and evaluation performance, while Anthropic’s Fable 5 documentation makes refusal behavior and fallback handling especially explicit.
That difference matters for enterprise deployments, because a model with stronger refusal controls may be preferable in sensitive environments, while a model that refuses less often or has a different safety posture may be easier to operate in some production workflows.
The right choice depends on policy tolerance, domain sensitivity, compliance requirements, and whether refusals create user-experience friction or necessary protection.
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CONTEXT AND OUTPUT LIMITS FAVOR CLAUDE FABLE 5 ON CLEARLY DOCUMENTED SPECIFICATIONS.
Claude Fable 5 has a publicly documented 1M token context window and up to 128k output tokens per request, which makes it a strong candidate for very large document and agentic workflows.
Anthropic states that Claude Fable 5 and Claude Mythos 5 share a 1M token context window by default and support up to 128k output tokens per request.
Those specifications are highly relevant for large document review, repository-scale coding, legal analysis, enterprise research, technical architecture work, and multi-step workflows where the model must keep extensive context available.
GPT-5.6 Sol is also positioned for long and complex professional workflows, and OpenAI reports long-context benchmark results across its GPT-5.6 family, but the most clearly stated direct context-window specification in the retrieved comparison material is the Anthropic Fable 5 line.
That does not mean Claude Fable 5 is automatically better at every long-context task.
It means Claude Fable 5 has an especially clear official specification for large-context use, while GPT-5.6 Sol’s case rests more heavily on benchmark performance, tool orchestration, cost efficiency, and OpenAI’s product surfaces.
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THE STRONGEST PRACTICAL SPLIT IS COST-EFFICIENT EXECUTION VERSUS LONG-HORIZON PREMIUM AGENTS.
GPT-5.6 Sol is easier to recommend when cost, speed, coding-agent performance, and production routing matter, while Claude Fable 5 is easier to recommend when long-context reasoning, analytical quality, and premium agent behavior matter more.
GPT-5.6 Sol should be the first model to test when the workflow involves agentic coding, terminal-style execution, tool-heavy automation, document generation, presentation-ready artifacts, high token volume, or budget-sensitive production deployment.
Claude Fable 5 should be the first model to test when the workflow involves long-horizon agentic work, massive context, high-quality analytical reasoning, enterprise-grade document analysis, sensitive workflows with explicit refusal handling, or benchmarks where Fable 5 is known to lead.
The correct choice is unlikely to come from the model name alone.
It should come from a small benchmark suite built around the actual workload, because these models trade places across coding, knowledge work, cost, and long-context performance.
For many teams, the best architecture may use both models: GPT-5.6 Sol for cost-efficient high-end execution, Claude Fable 5 for tasks where its analytical or long-horizon strengths justify the premium, and cheaper family members such as GPT-5.6 Terra or Luna for lower-risk steps.
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· Use GPT-5.6 Sol when cost-performance matters heavily.
· Use GPT-5.6 Sol when terminal-style coding and agentic execution matter most.
· Use Claude Fable 5 when long-horizon autonomous work matters most.
· Use Claude Fable 5 when the task benefits from very large context and strong analytical quality.
· Test both models on the actual workflow before choosing a production default.
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Best-fit summary
Use case | Better first model to test |
Cost-sensitive advanced workflows | GPT-5.6 Sol |
Agentic coding and terminal tasks | GPT-5.6 Sol |
SWE-Bench Pro-like coding tasks | Claude Fable 5 |
Long-horizon autonomous agents | Claude Fable 5 |
Presentation-heavy artifacts | GPT-5.6 Sol |
Analytical knowledge work | Claude Fable 5 |
Very large context workflows | Claude Fable 5 |
High-volume production routing | GPT-5.6 Sol |
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THE FINAL VERDICT: GPT-5.6 SOL IS THE BETTER VALUE PLAY, WHILE CLAUDE FABLE 5 REMAINS A PREMIUM AGENTIC POWERHOUSE.
GPT-5.6 Sol has the stronger cost-performance case, but Claude Fable 5 remains one of the hardest models to dismiss when long-running agents, analytical quality, and large-context work are central to the task.
GPT-5.6 Sol is cheaper per token, highly competitive in broad intelligence, strong in coding-agent evaluations, and compelling for teams that need frontier capability without paying Claude Fable 5’s higher API price.
Claude Fable 5 is more expensive, but it remains Anthropic’s most capable widely released model, with a documented 1M token context window, up to 128k output tokens, explicit support for long-horizon agentic work, and strong results in areas where analytical quality and sustained reasoning matter.
The benchmark evidence does not support a simple winner-takes-all conclusion.
GPT-5.6 Sol leads on several agentic and terminal-heavy coding signals, while Claude Fable 5 leads strongly on SWE-Bench Pro and remains ahead in some knowledge-work quality measurements.
The most practical conclusion is direct: choose GPT-5.6 Sol when efficiency, coding-agent performance, speed, and cost control matter most; choose Claude Fable 5 when long-context reasoning, long-running agents, analytical quality, and premium Claude behavior justify the higher price.
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