Coding work
Code generation, repair, and software-engineering tasks
Claude Fable 5
Claude Fable 5 leads on the public coding lane, 74.5 to 60.3, with Supported evidence for both models and non-overlapping 90% intervals.
Updated September 27, 2026. Rank says Claude Fable 5 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
Claude Fable 5 has the higher public score, 79.07 versus 67.66, and the 90% score intervals do not overlap. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Claude Fable 5
Claude Fable 5 leads on the public coding lane, 74.5 to 60.3, with Supported evidence for both models and non-overlapping 90% intervals.
Tool use, computer use, and multi-step task completion
Claude Fable 5
Claude Fable 5 leads on the public agentic lane, 73.7 to 58.6, with Supported evidence for both models and non-overlapping 90% intervals.
1K fresh input + 500 output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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.
Like-for-like · BenchAlign v5.7
Claude Fable 5 leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
ARC-AGI-2Reasoning
Normalized gap 4.6MMLU-Pro (Vals)Knowledge
Normalized gap 1.4LiveCodeBench (Vals)Coding
Normalized gap 1.1Each 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.
| Category | Claude Fable 5 | Gemini 3.7 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 73.7Supported · #4/105 | 58.6Supported · #20/105 | Like-for-likeBenchAlign v5.7 lane · 5 vs 7 public rows | Claude Fable 5 leads |
| Coding | 74.5Supported · #4/135 | 60.3Supported · #17/135 | Like-for-likeBenchAlign v5.7 lane · 10 vs 6 public rows | Claude Fable 5 leads |
| Knowledge | 81.8Supported · #4/158 | 71.1Supported · #10/158 | Like-for-likeBenchAlign v5.7 lane · 2 vs 6 public rows | Claude Fable 5 leads · intervals overlap |
| Reasoning | 80.6Unranked · 4 rankable rows | 77.8Unranked · 5 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | 62.6Unranked · 2 rankable rows | 82.6#10/50 | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 75.7#58/124 | 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.
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.
1K fresh input + 500 output tokens
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Fable 5
Gemini 3.7 Flash
Claude Fable 5
claude-fable-5
Anthropic model overviewGemini 3.7 Flash
gemini-3.7-flash
Google Gemini 3.7 Flash API documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5
$1 per 1M cached input tokens
Claude API pricingGemini 3.7 Flash
$0.075 per 1M cached input tokens
Google Gemini API pricingClaude Fable 5
text, image
Anthropic model overviewGemini 3.7 Flash
text, image, video, audio, pdf
Google Gemini 3.7 Flash API documentationClaude Fable 5
Gemini 3.7 Flash
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewGemini 3.7 Flash
Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity
Google DeepMind Gemini 3.7 Flash model cardClaude Fable 5
Reasoning
Gemini 3.7 Flash
Reasoning
Claude Fable 5
Proprietary
Gemini 3.7 Flash
Proprietary
Claude Fable 5
Proprietary
Gemini 3.7 Flash
Proprietary
Claude Fable 5
2026-06-09
Gemini 3.7 Flash
2026-08-13
Claude Fable 5 has the higher public score, 79.07 versus 67.66, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Fable 5 leads the public coding lane, 74.5 to 60.3, with Supported evidence for both models and non-overlapping 90% intervals.
Claude Fable 5 leads the public agentic tasks lane, 73.7 to 58.6, with Supported evidence for both models and non-overlapping 90% intervals.
For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.00263 on Gemini 3.7 Flash; repository review costs $0.65 and $0.04875; the cache-heavy agent loop costs $0.9 and $0.0675. Costs use the listed standard API rates.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Claude Fable 5 leads this result
Terminal-Bench 2.1
Gemini 3.7 Flash leads this result
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 5 leads this result
ApprenticeBench
Shared sourceClaude Fable 5 leads this result
AutomationBench
Not directly comparable
OSWorld 2.0
Not directly comparable
Agents' Last Exam
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
FrontierSWE v2
Shared sourceClaude Fable 5 leads this result
FrontierCode 1.1 Main
Claude Fable 5 leads this result
Terminal-Bench 2.1
Gemini 3.7 Flash leads this result
cursorBench31
Not directly comparable
cursorBench32
Not directly comparable
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
Claude Fable 5 leads this result
SWE-bench (Vals)
Claude Fable 5 leads this result
DeepSWE
Not directly comparable
Blueprint-Bench 2
Not directly comparable
OfficeQA Pro
Not directly comparable
CharXiv w/o tools
Not directly comparable
CharXiv
Not directly comparable
LVBench
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash leads this result
MMLU-Pro (Vals)
Claude Fable 5 leads this result
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
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Last updated September 27, 2026