Claude Opus 4.7 vs DeepSeek V4 Pro
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Claude Opus 4.7 | DeepSeek-V4-Pro |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.2 | #6 of 2278.9 score | UnrankedNot in the 22-model eligible cohort |
| CostLower is better · Published-token output estimate | #31 of 36$0.286 per LiveBench case | #9 of 36$0.054 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.0 | #12 of 2251.8 score | UnrankedNot in the 22-model eligible cohort |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | Claude Opus 4.7 | DeepSeek-V4-Pro |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,483.21100% of row best · rating · claude-opus-4-7; 95% CI [1479.33960820, 1487.07067536]; votes 61256; rank 10 | 1,451.0098% of row best · rating · deepseek-v4-pro; 95% CI [1447.00829245, 1454.98663953]; votes 54243; rank 41 |
| LiveBench2026-06-25 · overall · leader | 80.97100% of row best · percent · claude-opus-4-7-xhigh-effort · 11,460 output tokens / case | 76.7995% of row best · percent · deepseek-v4-pro · 35,014 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 2 benchmark winsOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Claude Opus 4.7 | DeepSeek-V4-Pro |
|---|---|---|
| Developer | Anthropic | DeepSeek |
| Family | Claude 4 | Deepseek V4 Pro |
| Model | Claude Opus 4.7 | DeepSeek-V4-Pro |
| Version | Claude Opus 4.7 | DeepSeek-V4-Pro |
| Lifecycle | active | active |
| Released | 2026-04-16 | 2026-04-24 |
| Knowledge cutoff | 2026-01-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,000K | 1,049K |
| Total parameters | Unknown | 1.6T |
| Active parameters | Unknown | 49B |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning |
14 comparable fields · 11 material differences · Pair passes the primary-source comparison gate
Claude Opus 4.7 Capabilities
DeepSeek V4 Pro Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
Claude Opus 4.7Anthropic | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Claude Opus 4.7Anthropic | family variantsimage, text |
Claude Opus 4.7Anthropic | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Claude Opus 4.7Anthropic | vs | Grok 4.6xAI | cross-developer peersimage, text |
Claude Opus 4.7Anthropic | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Claude Opus 4.7Anthropic | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Claude Opus 4.7Anthropic | cross-developer peersimage, text |
Claude Opus 4.7Anthropic | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Claude Opus 4.7Anthropic | vs |  Hy4 previewTencent | cross-developer peerstext |
Claude Opus 4.7Anthropic | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Claude Opus 4.7Anthropic | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Claude Opus 4.7Anthropic | vs | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.7 vs DeepSeek V4 Pro FAQs
Is Claude Opus 4.7 or DeepSeek V4 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.7 and DeepSeek V4 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.7 or DeepSeek V4 Pro?+
Claude Opus 4.7 is $5.00 and DeepSeek V4 Pro is $0.66 per million tokens, so DeepSeek V4 Pro is cheaper on this metric. Claude Opus 4.7 is $25.00 and DeepSeek V4 Pro is $1.5441 per million tokens, so DeepSeek V4 Pro is cheaper on this metric.
Which has a larger context window, Claude Opus 4.7 or DeepSeek V4 Pro?+
DeepSeek V4 Pro has the larger sourced context window. Claude Opus 4.7 supports 1,000K and DeepSeek V4 Pro supports 1,049K.
Which performs better in benchmarks, Claude Opus 4.7 or DeepSeek V4 Pro?+
Claude Opus 4.7 leads the current overall benchmark count. The result uses 2 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Claude Opus 4.7 or DeepSeek V4 Pro be self-hosted?+
DeepSeek V4 Pro is the only model in this pair currently marked as self-hostable. Claude Opus 4.7 is not marked open weight; DeepSeek V4 Pro is open weight.
Can Claude Opus 4.7 and DeepSeek V4 Pro understand images?+
Claude Opus 4.7 is documented with image input; DeepSeek V4 Pro is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 4.7 or DeepSeek V4 Pro?+
Neither has a larger sourced maximum output. Claude Opus 4.7 is 128K and DeepSeek V4 Pro is —.
Do Claude Opus 4.7 and DeepSeek V4 Pro support reasoning and tool use?+
Claude Opus 4.7: reasoning, tool calling, and image input. DeepSeek V4 Pro: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Opus 4.7 or DeepSeek V4 Pro?+
Claude Opus 4.7 has 2 sourced provider routes; DeepSeek V4 Pro has 6, so DeepSeek V4 Pro has broader tracked availability.
Which offers better value, Claude Opus 4.7 or DeepSeek V4 Pro?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.