Claude Opus 5.5 vs DeepSeek V4 Pro 0424
Full profiles: Claude Opus 5.5 (Anthropic) and DeepSeek V4 Pro 0424 (DeepSeek): every benchmark, price and rank.
Updated · Data from 10 sources
As of 22 Oct 2026, Claude Opus 5.5 (max) leads on quality (72.7 vs 59.7 on the BenchLeader Index), but DeepSeek V4 Pro 0424 (max) costs 15× less, so it is the better buy unless you need the extra quality. Claude Opus 5.5 leads in every category we measure both in.
Claude Opus 5.5 benchmarks max Anthropic
DeepSeek V4 Pro 0424 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 |
|---|---|---|---|
| Agents & tool use | Either | 64 vs 64 category score; GDPval-AA v2.1 68.3% vs 32.8% | Either |
| Reasoning | Claude Opus 5.5 | 76 vs 62 category score; CritPt 31.7% vs 12.9% | High |
| Knowledge | Claude Opus 5.5 | 79 vs 57 category score; AA-Omniscience 46.4 vs -10.7 | High |
| Long documents | Claude Opus 5.5 | 68 vs 62 category score; AA-LCR 84.7% vs 74.7% | Medium |
| Lowest cost | DeepSeek V4 Pro 0424 | $0.548 vs $8.00 per 1M blended; agentic coding session $0.030 vs $0.320 | – |
| Fastest first answer | DeepSeek V4 Pro 0424 | 614.4 s (Claude Opus 5.5 (max)) vs 49.0 s (DeepSeek V4 Pro 0424 (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 Opus 5.5 (max) leads on quality: 72.7 vs 59.7.
- Claude Opus 5.5 (max) is stronger in coding, composite, knowledge, long context, maths, multimodal, reasoning.
- DeepSeek V4 Pro 0424 (max) is stronger in agents & tools, instruction following.
- DeepSeek V4 Pro 0424 (max) is 15× cheaper ($0.548 vs $8.00 per 1M blended).
At a glance
| Spec | Claude Opus 5.5 | DeepSeek V4 Pro 0424 |
|---|---|---|
| Developer | Anthropic | DeepSeek |
| Released | 22 Sept 2026 | 24 Apr 2026 |
| Context window (tokens) | 1,000,000 | 1,000,000 |
| Max output (tokens) | 128,000 (up to 300K on the Message Batches API (beta)) | – |
| Input types | text, image | – |
| Reasoning effort settings | low, medium, high, xhigh, max | – |
| Compared at | Claude Opus 5.5 (max) | DeepSeek V4 Pro 0424 (max) |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $4.00 / $20.00 / $0.200 | $0.440 / $0.870 / – |
| API model ID | claude-opus-5-5 | – |
| Where to use it | Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry | – |
Limits, availability and announced prices from the vendors' own pages: docs.anthropic.com (checked 20 Oct 2026). Everything else is from the day's data.
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 Opus 5.5 (max) | DeepSeek V4 Pro 0424 (max) | Leader |
|---|---|---|---|
| Reasoning | 75.8 | 62.2 | Claude Opus 5.5 |
| Coding | 82.1 | – | not comparable |
| Agents & tools | 63.6 | 63.8 | level |
| Maths | 74.3 | – | not comparable |
| Knowledge | 78.8 | 56.7 | Claude Opus 5.5 |
| Instruction following | – | 74.5 | not comparable |
| Multimodal | 71.0 | – | not comparable |
| Long context | 67.5 | 62.3 | Claude Opus 5.5 |
| Composite | 94.3 | 63.7 | Claude Opus 5.5 |
Where sources disagree
Across the 14 benchmarks both have, Claude Opus 5.5 leads 13, DeepSeek V4 Pro 0424 leads 1. Every overall index that rates both puts Claude Opus 5.5 ahead.
| Overall index | Claude Opus 5.5 (max) | DeepSeek V4 Pro 0424 (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 72.7 | 59.7 | Claude Opus 5.5 |
| AA Intelligence Index v4.3.2 | 57.6 | 30.4 | Claude Opus 5.5 |
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 Opus 5.5 (max) | DeepSeek V4 Pro 0424 (max) |
|---|---|---|
| Blended price per 1M tokens | $8.00 | $0.548 |
| Input / output per 1M | $4.00 / $20.00 | $0.440 / $0.870 |
| Cached input per 1M | $0.200 | – |
| Price per task: chat reply | $0.0076 | $0.0004 |
| Price per task: summarize a 30-page report | $0.060 | $0.0058 |
| Price per task: code edit | $0.054 | $0.0039 |
| Price per task: agentic coding session | $0.320 | $0.030 |
| Price per task: structured extraction | $0.012 | $0.0011 |
| Cost to run a full benchmark suite | $5.98 | $0.122 |
| Output speed | 96 tok/s | 92 tok/s |
| Time to first answer | 614.4 s | 49.0 s |
| Time to a complete answer | 619.6 s | 54.4 s |
Per token is not per task: on Artificial Analysis' full suite Claude Opus 5.5 (max) cost $5.98 and DeepSeek V4 Pro 0424 (max) $0.122, because Claude Opus 5.5 (max) used about 3.4× as many tokens for the same work, which a per-token price hides.
Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 5.5 (max) scores 72.7 and takes 614 s to its first answer token; Claude Opus 5.5 (xhigh) scores 70.1 in 124 s, Claude Opus 5.5 (high) scores 69.5 in 35 s. DeepSeek V4 Pro 0424 (max) scores 59.7 and takes 49 s to its first answer token; DeepSeek V4 Pro 0424 (high) scores 57.9 in 25 s, DeepSeek V4 Pro 0424 (no reasoning) scores 50.8 in 2 s. 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
116 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 Opus 5.5 (max) | DeepSeek V4 Pro 0424 (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 72.7 | 59.7 |
| Agents & tools score | BenchLeader | 63.6 | 63.8 |
| Coding score | BenchLeader | 82.1 | – |
| Composite score | BenchLeader | 94.3 | 63.7 |
| Knowledge score | BenchLeader | 78.8 | 56.7 |
| Long context score | BenchLeader | 67.5 | 62.3 |
| Maths score | BenchLeader | 74.3 | – |
| Multimodal score | BenchLeader | 71.0 | – |
| Reasoning score | BenchLeader | 75.8 | 62.2 |
| Instruction following score | BenchLeader | – | 74.5 |
| Blended price $/M | BenchLeader | $8.00 | $0.548 |
| Output speed | BenchLeader | 96 tok/s | 92 tok/s |
| Time to first answer | BenchLeader | 614.4 s | 49.0 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,000,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.6% | – |
| Arena Hard Prompts | Arena (LMArena) | 1534† | – |
| LiveBench 2.0 Reasoning | LiveBench | 92.2%† | – |
| LiveBench 1.0 Reasoning | LiveBench | 92.2% | – |
| GPQA Diamond (AA) | Artificial Analysis | – | 88.8% |
| Humanity's Last Exam (AA) | Artificial Analysis | 61.4% | 37.5% |
| ARC-AGI-1 | ARC Prize | 97.5% | – |
| ARC-AGI-2 | ARC Prize | 91.7% | – |
| CritPt | Artificial Analysis | 31.7% | 12.9% |
| MysteryMechanism | Vals AI | 49.5%† | – |
| Chess Puzzles | Epoch AI Benchmarking Hub | 67.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 71.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 68.2% | – |
| DTBench | Epoch AI Benchmarking Hub | 98.9% | – |
| Coding | |||
| SciCode | SciCode | 66.9% | – |
| FrontierCode | Cognition | 54.6%† | – |
| Arena Coding | Arena (LMArena) | 1548† | – |
| Arena WebDev | Arena (LMArena) | 1807 | – |
| LiveBench 2.0 Coding | LiveBench | 96.3%† | – |
| LiveBench 1.0 Coding | LiveBench | 89.3% | – |
| SciCode (AA) | Artificial Analysis | 66.9% | 50.8% |
| IOI | Vals AI | 95.1%† | – |
| Code Migration | Vals AI | 66.7%† | – |
| ProgramBench | Vals AI | 18.5%† | – |
| Vibe Code Bench 1-100 | Vals AI | 30.4%† | – |
Show all 116 benchmarks
| Metric | Publisher | Claude Opus 5.5 (max) | DeepSeek V4 Pro 0424 (max) |
|---|---|---|---|
| Coding | |||
| Vibe Code Bench v1.1 | Vals AI | 90.3%† | – |
| MirrorCode | Epoch AI Benchmarking Hub | 77.4% | – |
| CursorBench | Cursor | 57.8% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 2147.4† | – |
| FrontierSWE | Epoch AI Benchmarking Hub | 62.3% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 59.6% | 14.7% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 64.0% |
| Startrise Frontend | Startrise | 71.1%† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 52.2% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 64.8% | – |
| Remote Labor Index | Scale AI / CAIS | 21.3%† | – |
| APEX-Agents | Mercor | 73.5% | – |
| Arena Agent | Arena (LMArena) | 20.9 | – |
| LiveBench Long Agentic | LiveBench | 85.0%† | – |
| LiveBench Browser | LiveBench | 80.0%† | – |
| LiveBench Agentic Coding | LiveBench | 71.7% | – |
| Terminal-Bench Hard | Artificial Analysis | – | 46.2% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 96.2% |
| Terminal-Bench 2.1 (Vals) | Vals AI | 87.6%† | – |
| HiL-Bench | Scale AI SEAL | 59.0%† | – |
| GDPval-AA v2.1 | Artificial Analysis | 68.3% | 32.8% |
| τ³-Banking (AA) | Artificial Analysis | – | 30.1% |
| ITBench SRE (AA) | Artificial Analysis | 38.2% | 38.3% |
| Analyst Agent (AA) | Artificial Analysis | 56.3% | 18.8% |
| APEX-Agents (AA) | Artificial Analysis | – | 24.3% |
| CUA-bench | Vals AI | 14.0% | – |
| CyberBench | Vals AI | 55.4%† | – |
| Finance Agent v2 | Vals AI | 58.6%† | – |
| Harvey's Legal Agent Benchmark | Vals AI | 9.2%† | – |
| Legal Research Bench | Vals AI | 50.5%† | – |
| SREBench | Vals AI | 33.6%† | – |
| Tax Agent Bench | Vals AI | 45.6%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 65.2%† | – |
| Terminal-Bench Science | Vals AI | 47.1%† | – |
| Time Horizon Index: KSP | Vals AI | 91.3%† | – |
| Arena Search | Arena (LMArena) | 1211† | – |
| Vending-Bench 2 | Andon Labs | 9235.3† | – |
| AutomationBench | Artificial Analysis | 69.5% | 56.3% |
| GDP.pdf (AA) | Artificial Analysis | 26.2% | 13.4% |
| Harvey LAB-AA v1.1 | Harvey | 4.2% | – |
| EnterpriseOps-Gym | Artificial Analysis | – | 40.4% |
| AA-Briefcase v1.1 | Artificial Analysis | 1802 | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 59.0% | – |
| CWE-Bench (AA) | Artificial Analysis | 58.3% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 26.9% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 0.8% | – |
| FACTS Search | 86.5%† | – | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 91.2% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 95.0% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 100.0% | – |
| ProofBench | Vals AI | 100.0% | – |
| LiveBench Mathematics | LiveBench | 97.1% | – |
| Arena Maths | Arena (LMArena) | 1512† | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 72.2% | – |
| LiveBench 2.0 Data Analysis | LiveBench | 80.3%† | – |
| LiveBench Research | LiveBench | 85.0%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 80.3% | – |
| AA-Omniscience | Artificial Analysis | 46.4 | -10.7 |
| PRBench Legal | Scale AI SEAL | 47.6%† | – |
| BioMysteryBench | Vals AI | 79.3%† | – |
| Excel Modeling Benchmark | Vals AI | 75.9%† | – |
| MedCode | Vals AI | 49.8%† | – |
| MedScribe | Vals AI | 91.4%† | – |
| Public Benefits Bench | Vals AI | 70.6%† | – |
| EBR-bench | Epoch AI Benchmarking Hub | 71.4% | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 30.6% | – |
| AA-Omniscience: accuracy | Artificial Analysis | 66.2% | 43.0% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 41.4% | 5.9% |
| Instruction following | |||
| LiveBench Language | LiveBench | 86.3% | – |
| LiveBench Instruction Following | LiveBench | 65.7% | – |
| IFBench | Artificial Analysis | – | 76.5% |
| Arena Instruction Following | Arena (LMArena) | 1520† | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1509† | – |
| Arena Creative Writing | Arena (LMArena) | 1520† | – |
| Arena Multi-turn | Arena (LMArena) | 1501† | – |
| Arena Longer Queries | Arena (LMArena) | 1524† | – |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1321† | – |
| LiveBench Design | LiveBench | 86.9%† | – |
| MMMU-Pro | Artificial Analysis | 87.7% | – |
| SAGE | Vals AI | 45.8%† | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 51.2%† | – |
| AA-3D | Artificial Analysis | 1191 | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 84.7% | 74.7% |
| MLCR | Artificial Analysis | 27.8% | 9.4% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 168.8† | – |
| LiveBench 2.0 | LiveBench | 86.5%† | – |
| LiveBench 1.0 | LiveBench | 83.2% | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 57.6 | 30.4 |
| Vals Index | Vals AI | 70.2† | – |
| AA Cyber Index | Artificial Analysis | 28.7 | – |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 5.8% | – |
Data as of 2026-10-22. Best configuration of each model; every score links to its source on the model pages.
What changed
- Claude Opus 5.5 (max): 2 score revisions.
- Claude Opus 5.5 (max): 2 score revisions.
- Claude Opus 5.5 (max): BenchLeader Index 71.9 → 72.6.
- Claude Opus 5.5 (max): 1 new result (FORTRESS).
- Claude Opus 5.5 (max): BenchLeader Index 71.3 → 71.9.
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 1 new result (Chess Puzzles); 1 score revision.
- Claude Opus 5.5 (max): 1 new result (Arena Agent).
- Claude Opus 5.5 (max): BenchLeader Index 72.0 → 71.3.
- Claude Opus 5.5 (max): 7 new results (FrontierCode 1.1 (AA), Terminal-Bench-Science 0.1 (AA), AA Cyber Index…).
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- Claude Opus 5.5 vs GPT-6.1 Sol
- Claude Opus 5.5 vs Claude Fable 5
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Claude Opus 5.5 vs DeepSeek V4 Pro 0424: questions
- Is Claude Opus 5.5 better than DeepSeek V4 Pro 0424?
- Claude Opus 5.5 (max) leads on quality: 72.7 vs 59.7. The BenchLeader Index combines every independent quality benchmark; Claude Opus 5.5 (max) is ahead overall as of 2026-10-22, but check the category scores for your use.
- Which is cheaper, Claude Opus 5.5 or DeepSeek V4 Pro 0424, and will that change?
- DeepSeek V4 Pro 0424 is cheaper today: $0.548 against $8.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Opus 5.5 better than DeepSeek V4 Pro 0424 for agents and tool use?
- They are level in agentic tasks (64 vs 64 on the category index).
- Which is better for long documents?
- Claude Opus 5.5 scores higher on long-context benchmarks (68 vs 62). Both accept 1,000,000 tokens of context.
- Which has lower latency?
- DeepSeek V4 Pro 0424 (max) answers first: 49.0 s against 614.4 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 Opus 5.5 or DeepSeek V4 Pro 0424?
- Claude Opus 5.5 streams faster: 96 against 92 output tokens per second.
- Do independent benchmarks agree on Claude Opus 5.5 vs DeepSeek V4 Pro 0424?
- Across the 14 benchmarks both have, Claude Opus 5.5 leads 13, DeepSeek V4 Pro 0424 leads 1. Every overall index that rates both puts Claude Opus 5.5 ahead.
- When was Claude Opus 5.5 released?
- Claude Opus 5.5 was released on 22 Sept 2026.