Coding work
Code generation, repair, and software-engineering tasks
Claude Fable 5.1
Claude Fable 5.1 leads on the public coding lane, 80 to 67, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Claude Fable 5.1 vs GPT-6.1 Sol
Updated September 29, 2026. Rank says Claude Fable 5.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Claude Fable 5.1 has the higher public score estimate, 82.5 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- Claude Fable 5.1 only
- 23
- GPT-6.1 Sol only
- 6
- Like-for-like categories
- 1 / 8
Supported: Claude Fable 5.1 · Estimated: GPT-6.1 SolHow the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
GPT-6.1 Sol
GPT-6.1 Sol has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
GPT-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
GPT-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
80.0Claude Fable 5.167.0GPT-6.1 Sol
Like-for-like · BenchAlign v5.7
Claude Fable 5.1 leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
23 Claude Fable 5.1 only
3 Shared
6 GPT-6.1 Sol only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
AutomationBenchAgentic
Normalized gap 4.7Claude Fable 5.1:31.4%GPT-6.1 Sol:36.1%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Like-for-like- Claude Fable 5.1
- 80.0
- Supported · #3/143
- GPT-6.1 Sol
- 67.0
- Supported · #8/143
- Basis
- BenchAlign v5.7 lane · 10 vs 1 public rows
- Reading
- Claude Fable 5.1 leads · intervals overlap
Knowledge
Directional only- Claude Fable 5.1
- 85.4
- Supported · #3/169
- GPT-6.1 Sol
- 71.3
- Estimated · #10/169
- Basis
- BenchAlign v5.7 lane · 4 vs 5 public rows
- Reading
- Directional only
Agentic
Not comparable- Claude Fable 5.1
- 78.9
- Supported · #2/117
- GPT-6.1 Sol
- Not ranked
- Basis
- BenchAlign v5.7 lane · 10 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Fable 5.1
- 81.4
- #3/27
- GPT-6.1 Sol
- 79.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-6.1 Sol
- 83.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-6.1 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-6.1 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-6.1 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5.1 | GPT-6.1 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 80.0Supported · #3/143 | 67.0Supported · #8/143 | Like-for-likeBenchAlign v5.7 lane · 10 vs 1 public rows | Claude Fable 5.1 leads · intervals overlap |
| Knowledge | 85.4Supported · #3/169 | 71.3Estimated · #10/169 | Directional onlyBenchAlign v5.7 lane · 4 vs 5 public rows | Directional only |
| Agentic | 78.9Supported · #2/117 | Not ranked | Not comparableBenchAlign v5.7 lane · 10 vs 3 public rows | Not comparable |
| Reasoning | 81.4#3/27 | 79.4Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 83.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Claude Fable 5.1
- $0.035
- Fits in one request
- GPT-6.1 Sol
- $0.007
- Fits in one request
GPT-6.1 Sol has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5.1
- $0.65
- Fits in one request
- GPT-6.1 Sol
- $0.13
- Fits in one request
GPT-6.1 Sol has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Fable 5.1
- $0.75
- Fits in one request
- GPT-6.1 Sol
- $0.16
- Fits in one request
GPT-6.1 Sol has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Fable 5.1GPT-6.1 Sol
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5.1
GPT-6.1 Sol
API model ID
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchGPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5.1
$0.25 per 1M cached input tokens
Anthropic Fable 5.1 launchGPT-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationDocumented inputs
Claude Fable 5.1
Not sourced
GPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationDocumented outputs
Claude Fable 5.1
Not sourced
GPT-6.1 Sol
Provider availability
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchGPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationReasoning profile
Claude Fable 5.1
Reasoning
GPT-6.1 Sol
Reasoning
Weight access
Claude Fable 5.1
Proprietary
GPT-6.1 Sol
Proprietary
License
Claude Fable 5.1
Proprietary
GPT-6.1 Sol
Proprietary
Release date
Claude Fable 5.1
2026-09-01
GPT-6.1 Sol
2026-09-29
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Claude Fable 5.1 has the higher public score estimate, 82.5 versus 66.86, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.65 vs $0.13. Cache-heavy agent loop: $0.75 vs $0.16.
- Context tradeoff
- GPT-6.1 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Fable 5.1 or GPT-6.1 Sol?
Claude Fable 5.1 has the higher public score estimate, 82.5 versus 66.86, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Fable 5.1 or GPT-6.1 Sol?
Claude Fable 5.1 leads the public coding lane, 80 to 67, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Fable 5.1 or GPT-6.1 Sol?
GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Fable 5.1 or GPT-6.1 Sol?
For the stated presets, chat costs $0.035 on Claude Fable 5.1 and $0.007 on GPT-6.1 Sol; repository review costs $0.65 and $0.13; the cache-heavy agent loop costs $0.75 and $0.16. Costs use the listed standard API rates.
Which has the larger context window, Claude Fable 5.1 or GPT-6.1 Sol?
GPT-6.1 Sol has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence32 rows
Agentic
Terminal-Bench 4.0
Claude Fable 5.155.80%SourceGPT-6.1 Sol—Not directly comparable
Terminal-Bench-Science 0.1
Claude Fable 5.152.6%SourceGPT-6.1 Sol57.0%SourceGPT-6.1 Sol leads this result
OSWorld 2.0
Claude Fable 5.141.7%SourceGPT-6.1 Sol—Not directly comparable
AutomationBench
Claude Fable 5.131.4%SourceGPT-6.1 Sol36.1%SourceGPT-6.1 Sol leads this result
Toolathlon-Verified
Claude Fable 5.177.8%SourceGPT-6.1 Sol—Not directly comparable
Toolathlon Verified Pass@3
Claude Fable 5.181.5%SourceGPT-6.1 Sol—Not directly comparable
Toolathlon Verified Pass³
Claude Fable 5.173.1%SourceGPT-6.1 Sol—Not directly comparable
Toolathlon Verified avg. turns
Claude Fable 5.123.7 turnsSourceGPT-6.1 Sol—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 5.185.0%SourceGPT-6.1 Sol—Not directly comparable
ApprenticeBench
Claude Fable 5.172%SourceGPT-6.1 Sol—Not directly comparable
ExploitGym
Claude Fable 5.1—GPT-6.1 Sol35.1%SourceNot directly comparable
Coding
Bug Hunt Bench
Claude Fable 5.143 fixesSourceGPT-6.1 Sol—Not directly comparable
SWE-bench Pro
Claude Fable 5.181.2%SourceGPT-6.1 Sol—Not directly comparable
SWE Multilingual
Claude Fable 5.189.1%SourceGPT-6.1 Sol—Not directly comparable
SWE Multimodal
Claude Fable 5.154.7%SourceGPT-6.1 Sol—Not directly comparable
DeepSWE
Claude Fable 5.167.4%SourceGPT-6.1 Sol71.9%SourceGPT-6.1 Sol leads this result
FrontierSWE v2
Claude Fable 5.156.3%SourceGPT-6.1 Sol—Not directly comparable
ProgramBench
Claude Fable 5.187.6%SourceGPT-6.1 Sol—Not directly comparable
cursorBench32
Claude Fable 5.173.4%SourceGPT-6.1 Sol—Not directly comparable
LiveCodeBench (Vals)
Claude Fable 5.190.5%SourceGPT-6.1 Sol—Not directly comparable
cursorBench40
Claude Fable 5.151.8%SourceGPT-6.1 Sol—Not directly comparable
Reasoning
Knowledge
HLE
Claude Fable 5.165%SourceGPT-6.1 Sol—Not directly comparable
HLE w/o tools
Claude Fable 5.160.9%SourceGPT-6.1 Sol—Not directly comparable
GPQA Diamond (Vals)
Claude Fable 5.193.4%SourceGPT-6.1 Sol—Not directly comparable
MMLU-Pro (Vals)
Claude Fable 5.192.4%SourceGPT-6.1 Sol—Not directly comparable
HealthBench (raw)
Claude Fable 5.1—GPT-6.1 Sol56.7%SourceNot directly comparable
HealthBench (length-adjusted)
Claude Fable 5.1—GPT-6.1 Sol58.5%SourceNot directly comparable
HealthBench Professional
Claude Fable 5.1—GPT-6.1 Sol64.2%SourceNot directly comparable
HealthBench Professional (raw)
Claude Fable 5.1—GPT-6.1 Sol67.2%SourceNot directly comparable
HealthBench Hard
Claude Fable 5.1—GPT-6.1 Sol36.2%SourceNot directly comparable
32 public results · 3 shared
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Last updated September 29, 2026