Claude Fable 5.1 vs DeepSeek-V4.1-Flash-1017
A direct, empirical evaluation of Claude Fable 5.1 (Anthropic) against DeepSeek-V4.1-Flash-1017 (DeepSeek).
Claude Fable 5.1
Developed by Anthropic · Proprietary Frontier parameters
Full-repository software refactoring, agentic tool workflows, formal verification, and 1.5M-token needle retrieval.
DeepSeek-V4.1-Flash-1017
Developed by DeepSeek · Open-Weight Distilled Transformer (MIT License) parameters
Top-tier open-weight coding (81.6% SWE-bench Verified), concise reasoning traces with 32% lower token bloat, and fast consumer GPU self-hosting.
| Metric | Claude Fable 5.1 | DeepSeek-V4.1-Flash-1017 |
|---|---|---|
| SWE-bench Verified | 82.8% | 81.6% |
| MMLU-Pro | 92.4% | 92.8% |
| MATH-500 | 97.9% | 94.8% |
| Context Window | 1,500,000 tokens | 128,000 tokens |
| Input Pricing (per 1M) | $12.00 | $0.14 |
| Output Pricing (per 1M) | $60.00 | $0.28 |
Architectural & Practical Deployment Analysis
Evaluating Claude Fable 5.1 (Anthropic) alongside DeepSeek-V4.1-Flash-1017 (DeepSeek) highlights key technical trade-offs across neural parameter scale and compute efficiency. Claude Fable 5.1 executes across Proprietary Frontier total parameters with Dynamic Recurrent MoE active per token, whereas DeepSeek-V4.1-Flash-1017 operates on Open-Weight Distilled Transformer (MIT License) parameters with Multi-Head Latent Attention (MLA) Core active per token.
On SWE-bench Verified software engineering tasks, Claude Fable 5.1 achieves 82.8% autonomous resolution rate, compared to 81.6% for DeepSeek-V4.1-Flash-1017. On complex reasoning benchmarks, Claude Fable 5.1 posts 92.4% on MMLU-Pro and 97.9% on MATH-500, against 92.8% and 94.8% respectively for DeepSeek-V4.1-Flash-1017.
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $264.00 on Claude Fable 5.1 versus $1.82 on DeepSeek-V4.1-Flash-1017.
Recommendation: Deploy Claude Fable 5.1 when your infrastructure prioritizes full-repository software refactoring, agentic tool workflows, formal verification, and 1.5m-token needle retrieval.. Deploy DeepSeek-V4.1-Flash-1017 when workload requirements favor top-tier open-weight coding (81.6% swe-bench verified), concise reasoning traces with 32% lower token bloat, and fast consumer gpu self-hosting..