Claude Opus 4.6 vs DeepSeek V4.1 Flash
Claude Opus 4.6 
DeepSeek V4.1 Flash Verdict
- Claude Opus 4.6 leads on quality: 63.7 vs 61.9.
- Claude Opus 4.6 is stronger in agents & tools, human preference, maths, multimodal.
- DeepSeek V4.1 Flash (max) is stronger in coding, reasoning, composite, knowledge, long context.
- DeepSeek V4.1 Flash (max) is 38× cheaper ($0.262 vs $10.00 per 1M blended).
- DeepSeek V4.1 Flash (max) streams 6.3× faster (215 vs 34 tokens per second).
| Metric | Claude Opus 4.6 | DeepSeek V4.1 Flash (max) |
|---|---|---|
| BenchLeader Index | 63.7 | 61.9 |
| Agents & tools score | 67.8 | 59.6 |
| Coding score | 62.3 | 68.9 |
| Human preference score | 64.7 | – |
| Maths score | 68.7 | – |
| Multimodal score | 67.0 | 61.4 |
| Reasoning score | 62.6 | 69.5 |
| Composite score | – | 74.5 |
| Knowledge score | – | 61.7 |
| Long context score | – | 68.5 |
| Blended price $/M | $10.00 | $0.262 |
| Output speed | 34 tok/s | 215 tok/s |
| Time to first answer | 1.7 s | 10.5 s |
| Context window | 1M | 1M |
| GPQA Diamond | 90.5% | – |
| OTIS Mock AIME | 94.4% | – |
| SWE-bench Verified (Epoch) | 78.7% | – |
| Humanity's Last Exam | 19.0% | – |
| Terminal-Bench | 79.8% | – |
| SimpleBench | 67.6% | – |
| Cybench | 93.0% | – |
| Remote Labor Index | 4.2% | – |
| WeirdML | 77.9% | – |
| APEX-Agents | 32.4% | – |
| FrontierCode | 26.6% | – |
| GSO-Bench | 33.3% | – |
| Epoch Capabilities Index | 155.3 | – |
| LMArena Text | 1497 | – |
| LMArena Hard Prompts | 1527 | – |
| LMArena Coding | 1546 | – |
| LMArena WebDev | 1537 | 1614 |
| LMArena Vision | 1312 | – |
| LMArena Agent | – | 4.9 |
| LiveBench | – | 81.1% |
| LiveBench Reasoning | – | 86.7% |
| LiveBench Coding | – | 80.0% |
| LiveBench Agentic Coding | – | 77.3% |
| LiveBench Mathematics | – | 93.3% |
| LiveBench Data Analysis | – | 79.3% |
| LiveBench Language | – | 81.2% |
| LiveBench Instruction Following | – | 70.0% |
| AA Intelligence Index | – | 39.5 |
| AA-LCR | – | 84.0% |
| MMMU-Pro | – | 77.0% |
| AA-Omniscience | – | -5.3 |
| Humanity's Last Exam (AA) | – | 39.3% |
| SciCode (AA) | – | 51.9% |
| HiL-Bench | 38.3% | – |
| EQ-Bench 4 | 1223 | – |
| Kagi LLM Benchmark | 72.4% | – |
| SWE-bench Verified (bash only) | 75.6% | – |
| SWE-bench Verified (any scaffold) | 75.6% | – |
| CritPt | – | 14.3% |
| GDPval (AA) | – | 56.6% |
| MedCode | 48.2% | – |
| MedScribe | 86.7% | – |
| Vibe Code Bench v1.1 | 57.6% | – |
| SWE Atlas: Codebase QnA | 33.3% | – |
| SWE Atlas: Refactoring | 35.6% | – |
| SWE Atlas: Test Writing | 36.7% | – |
| LMArena Maths | 1507 | – |
| LMArena Creative Writing | 1479 | – |
| LMArena Instruction Following | 1500 | – |
| LMArena Multi-turn | 1511 | – |
| LMArena Longer Queries | 1517 | – |
| LMArena Document | 1494 | – |
| Chess Puzzles | 17.0% | – |
| Mystery Game Puzzles | 15.0% | – |
| CL-bench | 20.7% | – |
| CL-bench Life | 13.6% | – |
| METR Time Horizons | 78.9% | – |
| ForecastBench | 60.0% | – |
| GBAEval | 44.1% | – |
| ALE-Bench | 996.5 | – |
| Vending-Bench 2 | 8017.6 | – |
Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.
Claude Opus 4.6 vs DeepSeek V4.1 Flash: questions
- Is Claude Opus 4.6 better than DeepSeek V4.1 Flash?
- Claude Opus 4.6 leads on quality: 63.7 vs 61.9. The BenchLeader Index combines every independent quality benchmark; Claude Opus 4.6 is ahead overall as of 2026-09-19, but check the category scores for your use.
- Is Claude Opus 4.6 better than DeepSeek V4.1 Flash for coding?
- DeepSeek V4.1 Flash scores higher in coding (69 vs 62 on the category index, where 50 is average).
- Is Claude Opus 4.6 better than DeepSeek V4.1 Flash for agentic tasks?
- Claude Opus 4.6 scores higher in agentic tasks (68 vs 60 on the category index, where 50 is average).
- Which is cheaper, Claude Opus 4.6 or DeepSeek V4.1 Flash?
- DeepSeek V4.1 Flash is cheaper: $0.262 against $10.00 per million tokens, blended at three input tokens per output token.
- Which is faster, Claude Opus 4.6 or DeepSeek V4.1 Flash?
- DeepSeek V4.1 Flash streams faster: 215 against 34 output tokens per second.
- Which has the larger context window?
- Both accept 1M tokens of context.