Claude Fable 5 vs DeepSeek V4.1 Flash 1017
Full profiles: Claude Fable 5 (Anthropic) and DeepSeek V4.1 Flash 1017 (DeepSeek): every benchmark, price and rank.
Updated · Data from 10 sources
As of 24 Oct 2026, Claude Fable 5 (max) and DeepSeek V4.1 Flash 1017 (max) are neck and neck on quality (69.3 vs 64.0 on the BenchLeader Index, inside the ±5.8 margin of error), and DeepSeek V4.1 Flash 1017 (max) costs 38× less, which makes it the better buy for most workloads. Claude Fable 5 leads in every category we measure both in.
Claude Fable 5 benchmarks max Anthropic
DeepSeek V4.1 Flash 1017 benchmarks max DeepSeekOpen in the comparison toolWhich to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Claude Fable 5 | 74 vs 70 category score; SciCode (AA) 61.0% vs 55.9% | Medium |
| Agents & tool use | Claude Fable 5 | 65 vs 63 category score; GDP.pdf (AA) 24.0% vs 17.2% | Medium |
| Maths | Claude Fable 5 | 72 vs 62 category score; FrontierMath Tiers 1–3 87.0% vs 71.2% | High |
| Reasoning | Claude Fable 5 | 74 vs 71 category score; ARC-AGI-2 89.2% vs 78.1% | Medium |
| Knowledge | Claude Fable 5 | 81 vs 57 category score; AA-Omniscience 43.3 vs 4.7 | High |
| Long documents | Either | 66 vs 66 category score; AA-LCR 82.3% vs 82.7% | Either |
| Lowest cost | DeepSeek V4.1 Flash 1017 | $0.525 vs $20.00 per 1M blended; agentic coding session $0.023 vs $0.800 | – |
| Fastest first answer | DeepSeek V4.1 Flash 1017 | 11.2 s (DeepSeek V4.1 Flash 1017 (max)) vs 59.8 s (Claude Fable 5 (max)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Claude Fable 5 (max) and DeepSeek V4.1 Flash 1017 (max) are neck and neck on quality (69.3 vs 64.0, within the ±5.8 margin).
- Claude Fable 5 (max) is stronger in agents & tools, coding, composite, instruction following, knowledge, maths, reasoning.
- DeepSeek V4.1 Flash 1017 (max) is stronger in long context, multimodal.
- DeepSeek V4.1 Flash 1017 (max) is 38× cheaper ($0.525 vs $20.00 per 1M blended).
- DeepSeek V4.1 Flash 1017 (max) streams 2.8× faster (198 vs 70 tokens per second).
At a glance
| Spec | Claude Fable 5 | DeepSeek V4.1 Flash 1017 |
|---|---|---|
| Developer | Anthropic | DeepSeek |
| Released | 9 Jun 2026 | 17 Oct 2026 |
| Context window (tokens) | 1,000,000 | 1,048,576 |
| Compared at | Claude Fable 5 (max) | DeepSeek V4.1 Flash 1017 (max) |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $10.00 / $50.00 / $1.00 | $0.300 / $1.20 / $0.0060 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Claude Fable 5 (max) | DeepSeek V4.1 Flash 1017 (max) | Leader |
|---|---|---|---|
| Reasoning | 73.8 | 70.6 | Claude Fable 5 |
| Coding | 73.8 | 70.1 | Claude Fable 5 |
| Agents & tools | 65.0 | 63.0 | Claude Fable 5 |
| Maths | 72.4 | 61.8 | Claude Fable 5 |
| Knowledge | 81.0 | 57.1 | Claude Fable 5 |
| Instruction following | 63.1 | – | not comparable |
| Multimodal | – | 63.7 | not comparable |
| Long context | 66.3 | 66.4 | level |
| Composite | 85.0 | 80.0 | Claude Fable 5 |
Where sources disagree
Across the 20 benchmarks both have, Claude Fable 5 leads 15, DeepSeek V4.1 Flash 1017 leads 5. Every overall index that rates both puts Claude Fable 5 ahead.
| Overall index | Claude Fable 5 (max) | DeepSeek V4.1 Flash 1017 (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 69.3 | 64.0 | neck and neck |
| AA Intelligence Index v4.3.2 | 49.6 | 45.1 | Claude Fable 5 |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | Claude Fable 5 (max) | DeepSeek V4.1 Flash 1017 (max) |
|---|---|---|
| Blended price per 1M tokens | $20.00 | $0.525 |
| Input / output per 1M | $10.00 / $50.00 | $0.300 / $1.20 |
| Cached input per 1M | $1.00 | $0.0060 |
| Price per task: chat reply | $0.019 | $0.0005 |
| Price per task: summarize a 30-page report | $0.150 | $0.0043 |
| Price per task: code edit | $0.135 | $0.0036 |
| Price per task: agentic coding session | $0.800 | $0.023 |
| Price per task: structured extraction | $0.030 | $0.0008 |
| Cost to run a full benchmark suite | $8.75 | $0.262 |
| Output speed | 70 tok/s | 198 tok/s |
| Time to first answer | 59.8 s | 11.2 s |
| Time to a complete answer | 67.0 s | 13.7 s |
Per token is not per task: on Artificial Analysis' full suite Claude Fable 5 (max) cost $8.75 and DeepSeek V4.1 Flash 1017 (max) $0.262, because DeepSeek V4.1 Flash 1017 (max) used about 1.1× as many tokens for the same work.
Each model is compared at its best-ranked reasoning-effort setting. Claude Fable 5 (max) scores 69.3 and takes 60 s to its first answer token; Claude Fable 5 scores 67.1 in 4 s, Claude Fable 5 (high) scores 64.6 in 4 s. DeepSeek V4.1 Flash 1017 (max) scores 64.0 and takes 11 s to its first answer token. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
139 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Claude Fable 5 (max) | DeepSeek V4.1 Flash 1017 (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 69.3 | 64.0 |
| Agents & tools score | BenchLeader | 65.0 | 63.0 |
| Coding score | BenchLeader | 73.8 | 70.1 |
| Composite score | BenchLeader | 85.0 | 80.0 |
| Instruction following score | BenchLeader | 63.1 | – |
| Knowledge score | BenchLeader | 81.0 | 57.1 |
| Long context score | BenchLeader | 66.3 | 66.4 |
| Maths score | BenchLeader | 72.4 | 61.8 |
| Reasoning score | BenchLeader | 73.8 | 70.6 |
| Multimodal score | BenchLeader | – | 63.7 |
| Blended price $/M | BenchLeader | $20.00 | $0.525 |
| Output speed | BenchLeader | 70 tok/s | 198 tok/s |
| Time to first answer | BenchLeader | 59.8 s | 11.2 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,048,576 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 85.9% | 93.2% |
| SimpleBench | SimpleBench | 81.9% | – |
| Arena Hard Prompts | Arena (LMArena) | 1529† | – |
| LiveBench 2.0 Reasoning | LiveBench | 89.7%† | 89.1%† |
| LiveBench 1.0 Reasoning | LiveBench | 89.7% | – |
| GPQA Diamond (AA) | Artificial Analysis | 92.6% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 55.5% | 43.3% |
| GPQA Diamond (Vals) | Vals AI | 93.2%† | – |
| EnigmaEval | Scale AI SEAL | 39.3%† | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 88.8%† | – |
| ARC-AGI-1 | ARC Prize | 98.5% | 93.5% |
| ARC-AGI-2 | ARC Prize | 89.2% | 78.1% |
| CritPt | Artificial Analysis | 28.6% | 24.0% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 47.0% | 46.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 52.0% | 49.0% |
| LMCA | Epoch AI Benchmarking Hub | 60.3% | – |
| DTBench | Epoch AI Benchmarking Hub | 98.4% | – |
| ForecastBench | ForecastBench | 66.0% | – |
| BTF-3 | Epoch AI Benchmarking Hub | 13.0%† | – |
| Coding | |||
| SciCode | SciCode | 61.0% | – |
| WeirdML | WeirdML | 91.9% | – |
| FrontierCode | Cognition | 53.5%† | – |
| GSO-Bench | GSO-Bench | 78.4%† | – |
| Arena Coding | Arena (LMArena) | 1551† | – |
| Arena WebDev | Arena (LMArena) | 1624† | 1664 |
Show all 139 benchmarks
| Metric | Publisher | Claude Fable 5 (max) | DeepSeek V4.1 Flash 1017 (max) |
|---|---|---|---|
| Coding | |||
| LiveBench 2.0 Coding | LiveBench | 98.3%† | 96.0%† |
| LiveBench 1.0 Coding | LiveBench | 86.0% | – |
| SciCode (AA) | Artificial Analysis | 61.0% | 55.9% |
| LiveCodeBench | Vals AI | 89.8%† | – |
| SWE-bench (Vals) | Vals AI | 95.0%† | – |
| Code Migration | Vals AI | 55.1%† | 44.2% |
| Code Migration v1.1 | Vals AI | – | 67.5% |
| ProgramBench | Vals AI | 2.0%† | – |
| Vibe Code Bench v1.1 | Vals AI | 90.3%† | 87.7% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 39.0%† | – |
| SWE Atlas: Refactoring | Scale AI SEAL | 54.8%† | – |
| SWE Atlas: Test Writing | Scale AI SEAL | 55.6%† | – |
| MirrorCode | Epoch AI Benchmarking Hub | 63.9%† | – |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | 95.0%† | – |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 69.7% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 2041.3† | – |
| FrontierSWE | Epoch AI Benchmarking Hub | 47.0% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 42.4% | 33.8% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 84.6% | – |
| Startrise Frontend | Startrise | 73.8%† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 43.7% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 44.5% | – |
| Remote Labor Index | Scale AI / CAIS | 15.8%† | – |
| APEX-Agents | Mercor | 63.6%† | – |
| Arena Agent | Arena (LMArena) | 8.3† | – |
| LiveBench Long Agentic | LiveBench | 53.2%† | 50.9%† |
| LiveBench Browser | LiveBench | 92.0%† | 76.0%† |
| LiveBench Agentic Coding | LiveBench | 62.2% | – |
| Terminal-Bench Hard | Artificial Analysis | 62.9% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 98.5% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | 80.5%† | – |
| MCP Atlas | Scale AI SEAL | 83.3%† | – |
| HiL-Bench | Scale AI SEAL | 56.3%† | – |
| τ³-bench Banking | τ²-bench | 39.7% | – |
| GDPval-AA v2.1 | Artificial Analysis | – | 62.3% |
| τ³-Banking (AA) | Artificial Analysis | 38.1% | – |
| Analyst Agent (AA) | Artificial Analysis | 48.8% | – |
| Finance Agent v2 | Vals AI | 56.3%† | 57.0% |
| Finance Agent v2.1 | Vals AI | – | 62.9% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | – | 14.2% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 11.3%† | 9.2% |
| Legal Research Bench | Vals AI | 49.5%† | 45.2% |
| Tax Agent Bench | Vals AI | 38.3%† | 44.4% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 41.4%† | 31.8% |
| Terminal-Bench Science | Vals AI | 15.7%† | – |
| Web Search Index | Vals AI | 46.9%† | – |
| Arena Search | Arena (LMArena) | 1229† | – |
| PostTrainBench | Epoch AI Benchmarking Hub | 41.8% | – |
| GBAEval | Epoch AI Benchmarking Hub | 74.5%† | – |
| Vending-Bench 2 | Andon Labs | 4966.6 | – |
| AutomationBench | Artificial Analysis | 54.1% | 66.6% |
| GDP.pdf (AA) | Artificial Analysis | 24.0% | 17.2% |
| EnterpriseOps-Gym | Artificial Analysis | 51.1% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1537 | 1570 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 12.9% |
| CWE-Bench (AA) | Artificial Analysis | – | 69.2% |
| DeepSec-Bench (AA) | Artificial Analysis | – | 24.9% |
| CyberGym end-to-end (AA) | Artificial Analysis | – | 76.3% |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 87.0% | 71.2% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 90.2% | 46.3% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 99.7% | 99.4% |
| ProofBench | Vals AI | 95.0% | – |
| LiveBench Mathematics | LiveBench | 96.0% | – |
| Arena Maths | Arena (LMArena) | 1521† | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 70.7%† | 42.5% |
| LiveBench 2.0 Data Analysis | LiveBench | 80.5%† | 79.5%† |
| LiveBench Research | LiveBench | 86.9%† | 79.5%† |
| LiveBench 1.0 Data Analysis | LiveBench | 80.5% | – |
| AA-Omniscience | Artificial Analysis | 43.3 | 4.7 |
| MMLU-Pro | Vals AI | 91.5%† | – |
| LegalBench | Vals AI | 88.6%† | – |
| CorpFin | Vals AI | 71.8%† | – |
| TaxEval | Vals AI | 76.9%† | – |
| PRBench Finance | Scale AI SEAL | 53.9%† | – |
| PRBench Legal | Scale AI SEAL | 52.6%† | – |
| Excel Modeling Benchmark | Vals AI | 73.7%† | 61.8% |
| Excel Modeling Benchmark v1.1 | Vals AI | – | 69.2% |
| MedCode | Vals AI | 56.1%† | – |
| MedScribe | Vals AI | 88.5%† | – |
| Public Benefits Bench v1.1 | Vals AI | 70.4%† | – |
| EBR-bench | Epoch AI Benchmarking Hub | 39.5% | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 29.8% | – |
| AA-Omniscience: accuracy | Artificial Analysis | 65.3% | 42.9% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 36.4% | 33.3% |
| Instruction following | |||
| LiveBench Language | LiveBench | 90.7% | – |
| LiveBench Instruction Following | LiveBench | 75.8% | – |
| IFBench | Artificial Analysis | 63.5% | – |
| Arena Instruction Following | Arena (LMArena) | 1508† | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1503† | – |
| EQ-Bench 4 | EQ-Bench | 1340† | – |
| Arena Creative Writing | Arena (LMArena) | 1501† | – |
| Arena Multi-turn | Arena (LMArena) | 1517† | – |
| Arena Longer Queries | Arena (LMArena) | 1522† | – |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1325† | – |
| LiveBench Design | LiveBench | 84.7%† | 83.4%† |
| MMMU-Pro | Artificial Analysis | – | 80.6% |
| MMMU-Pro (Vals) | Vals AI | 89.3%† | – |
| MortgageTax | Vals AI | 68.9%† | – |
| SAGE | Vals AI | 51.9%† | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 38.6%† | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 82.3% | 82.7% |
| Arena Document | Arena (LMArena) | 1499† | – |
| MLCR | Artificial Analysis | 20.6% | 26.7% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 163.4† | – |
| LiveBench 2.0 | LiveBench | 83.6%† | 79.2%† |
| LiveBench 1.0 | LiveBench | 83.0% | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 49.6 | 45.1 |
| Vals Index | Vals AI | – | 61.3 |
| Vals Finance Index | Vals AI | – | 60.9% |
| Vals RSI Index | Vals AI | 24.3% | – |
| Vals Multimodal Index | Vals AI | 74.2%† | – |
| Vals Router Index | Vals AI | – | 61.3%† |
| AA Cyber Index | Artificial Analysis | – | 56.8 |
| Metadata | |||
| AA Openness Index | Artificial Analysis | – | 44.4 |
Data as of 2026-10-24. Best configuration of each model; every score links to its source on the model pages.
What changed
- Claude Fable 5 (max): 1 new result (Vals RSI Index).
- DeepSeek V4.1 Flash 1017 (max): 10 new results (MLCR, AA Cyber Index, CWE-Bench (AA)…).
- DeepSeek V4.1 Flash 1017 (max): BenchLeader Index 63.1 → 63.9.
- Claude Fable 5 (max): 1 score revision.
- DeepSeek V4.1 Flash 1017 (max): 11 new results (GPQA Diamond, FrontierMath Tiers 1–3, FrontierMath Tier 4…).
- Claude Fable 5 (max): 1 new result (ForecastBench).
- DeepSeek V4.1 Flash 1017 (max): 1 new result (FrontierCode 1.1 (AA)); 2 score revisions.
- Claude Fable 5 (max): 4 new results (GDPval-AA v2.1, AA-Omniscience: accuracy, AA-Omniscience: non-hallucination…).
- Claude Fable 5 (max): 1 score revision.
- Claude Fable 5 (max): BenchLeader Index 64.6 → 69.1.
Related comparisons
- Claude Fable 5 vs Claude Opus 5.5
- Claude Fable 5 vs Gemini 4 Argon
- Claude Fable 5 vs Muse Wonder Full
- DeepSeek V4.1 Flash 1017 vs Claude Opus 5.5
- DeepSeek V4.1 Flash 1017 vs Gemini 4 Argon
- DeepSeek V4.1 Flash 1017 vs Muse Wonder Full
- DeepSeek V4.1 Flash 1017 vs Claude Fable 5.1
- Claude Opus 4.8 vs DeepSeek V4.1 Flash 1017
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Claude Fable 5 vs DeepSeek V4.1 Flash 1017: questions
- Is Claude Fable 5 better than DeepSeek V4.1 Flash 1017?
- Claude Fable 5 (max) and DeepSeek V4.1 Flash 1017 (max) are neck and neck on quality (69.3 vs 64.0, within the ±5.8 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-24, so check the category scores for your use.
- Which is cheaper, Claude Fable 5 or DeepSeek V4.1 Flash 1017, and will that change?
- DeepSeek V4.1 Flash 1017 is cheaper today: $0.525 against $20.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Fable 5 better than DeepSeek V4.1 Flash 1017 for coding?
- Claude Fable 5 scores higher in coding (74 vs 70 on the category index, where 50 is average).
- Is Claude Fable 5 better than DeepSeek V4.1 Flash 1017 for agents and tool use?
- Claude Fable 5 scores higher in agentic tasks (65 vs 63 on the category index, where 50 is average).
- Which is better for long documents?
- They are level on long-context benchmarks (66 vs 66). Both take about 1M tokens: Claude Fable 5 1,000,000, DeepSeek V4.1 Flash 1017 1,048,576.
- Which has lower latency?
- DeepSeek V4.1 Flash 1017 (max) answers first: 11.2 s against 59.8 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Which is faster, Claude Fable 5 or DeepSeek V4.1 Flash 1017?
- DeepSeek V4.1 Flash 1017 streams faster: 198 against 70 output tokens per second.
- Do independent benchmarks agree on Claude Fable 5 vs DeepSeek V4.1 Flash 1017?
- Across the 20 benchmarks both have, Claude Fable 5 leads 15, DeepSeek V4.1 Flash 1017 leads 5. Every overall index that rates both puts Claude Fable 5 ahead.
- When was DeepSeek V4.1 Flash 1017 released?
- DeepSeek V4.1 Flash 1017 was released on 17 Oct 2026.