Agentic
Not comparable- Claude Fable 5.1
- 80.1
- Supported · #1/152
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 9 vs 8 public rows
- Reading
- Not comparable
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V4.1 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
Each row shows the public-lane category score for both models: the BenchAlign 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 5.1 | DeepSeek V4.1 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 80.1Supported · #1/152 | Not ranked | Not comparableBenchAlign lane · 9 vs 8 public rows | Not comparable |
| Coding | 84.2Supported · #1/151 | Not ranked | Not comparableBenchAlign lane · 9 vs 6 public rows | Not comparable |
| Reasoning | 79.4#2/18 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 86.8Supported · #1/182 | Not ranked | Not comparableBenchAlign lane · 4 vs 3 public rows | Not comparable |
| Math | Not ranked | Not ranked | 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 |
| Multimodal | 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 |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
HLE
Knowledge
AutomationBench
Agentic
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
DeepSeek V4.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 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 5.1
DeepSeek V4.1 Flash
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchDeepSeek V4.1 Flash
deepseek-flash
DeepSeek models and pricingA 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 launchDeepSeek V4.1 Flash
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingClaude Fable 5.1
Not sourced
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideClaude Fable 5.1
Not sourced
DeepSeek V4.1 Flash
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchDeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseClaude Fable 5.1
Reasoning
DeepSeek V4.1 Flash
Reasoning
Claude Fable 5.1
Proprietary
DeepSeek V4.1 Flash
Open Weight
Claude Fable 5.1
Proprietary
DeepSeek V4.1 Flash
Open Weight
Claude Fable 5.1
2026-09-01
DeepSeek V4.1 Flash
2026-09-10
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.0
Claude Fable 5.1 leads this result
Terminal-Bench-Science 0.1
Not directly comparable
OSWorld 2.0
Not directly comparable
AutomationBench
DeepSeek V4.1 Flash leads this result
Toolathlon-Verified
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
HLE w/ tools
Not directly comparable
Agents' Last Exam
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
DeepSeek V4.1 Flash leads this result
FrontierSWE v2
Not directly comparable
ProgramBench
Claude Fable 5.1 leads this result
cursorBench32
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
Codeforces
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
NL2Repo
Not directly comparable
HLE
Claude Fable 5.1 leads this result
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
Apex
Not directly comparable
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.
DeepSeek V4.1 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.035 on Claude Fable 5.1 and $0.0009 on DeepSeek V4.1 Flash; repository review costs $0.65 and $0.0186; the cache-heavy agent loop costs $0.75 and $0.0192. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Last updated September 10, 2026
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