Claude Sonnet 4.5 vs DeepSeek V4 Pro 0813
At a Glance
| Compare | Claude Sonnet 4.5Anthropic |  DeepSeek V4 Pro 0813DeepSeek |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #36 of 4627.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 18.5–51.8 | #20 of 4665.0 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #20 of 44$0.094 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #14 of 3857.4 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $3.00Anthropic ↗ · Sep 3, 2026 | $1.30Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $15.00Anthropic ↗ · Sep 3, 2026 | $2.60Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 200K | 1,049K |
| Model facts checked | Sep 3, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Claude Sonnet 4.5 | DeepSeek-V4-Pro-0813 |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 63.6770% of row best · percent · Claude Sonnet 4.5 (Thinking 32K) | 90.50100% of row best · percent · DeepSeek V4 Pro 0813 (Low) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 13.6122% of row best · percent · Claude Sonnet 4.5 (Thinking 32K) | 61.25100% of row best · percent · DeepSeek V4 Pro 0813 (Max) |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,433.9499% of row best · rating · claude-sonnet-4-5-20250929-high-32k; 95% CI [1431.13982742, 1436.74901697]; votes 80980; rank 81 | 1,443.95100% of row best · rating · deepseek-v4-pro-high-20260813; 95% CI [1437.16050212, 1450.73220317]; votes 9008; rank 58 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 77.2591% of row best · points · Claude Sonnet 4.5 · 7,415 output tokens / case | 84.68100% of row best · points · DeepSeek V4 Pro 0813 (max) · 18,462 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 3 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Claude Sonnet 4.5 | DeepSeek-V4-Pro-0813 |
|---|---|---|
| Developer | Anthropic | DeepSeek |
| Family | Claude 4 | Deepseek V4 Pro |
| Model | Claude Sonnet 4.5 | DeepSeek-V4-Pro-0813 |
| Version | Claude Sonnet 4.5 | DeepSeek-V4-Pro-0813 |
| Lifecycle | active | active |
| Released | 2025-09-29 | 2026-08-13 |
| Knowledge cutoff | 2025-01-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 200K | 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), Fireworks Ai (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | agents, chat, coding, generation, reasoning, tools |
Claude Sonnet 4.5 Capabilities
DeepSeek V4 Pro 0813 Capabilities
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Claude Sonnet 4.5Anthropic | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Sonnet 4.5 vs DeepSeek V4 Pro 0813 FAQs
Is Claude Sonnet 4.5 or DeepSeek V4 Pro 0813 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.5 and DeepSeek V4 Pro 0813, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Sonnet 4.5 or DeepSeek V4 Pro 0813?+
Claude Sonnet 4.5 is $3.00 and DeepSeek V4 Pro 0813 is $1.30 per million tokens, so DeepSeek V4 Pro 0813 is cheaper on this metric. Claude Sonnet 4.5 is $15.00 and DeepSeek V4 Pro 0813 is $2.60 per million tokens, so DeepSeek V4 Pro 0813 is cheaper on this metric.
Which has a larger context window, Claude Sonnet 4.5 or DeepSeek V4 Pro 0813?+
DeepSeek V4 Pro 0813 has the larger sourced context window. Claude Sonnet 4.5 supports 200K and DeepSeek V4 Pro 0813 supports 1,049K.
Which performs better in benchmarks, Claude Sonnet 4.5 or DeepSeek V4 Pro 0813?+
DeepSeek V4 Pro 0813 leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Claude Sonnet 4.5 or DeepSeek V4 Pro 0813 be self-hosted?+
DeepSeek V4 Pro 0813 is the only model in this pair currently marked as self-hostable. Claude Sonnet 4.5 is not marked open weight; DeepSeek V4 Pro 0813 is open weight.
Can Claude Sonnet 4.5 and DeepSeek V4 Pro 0813 understand images?+
Claude Sonnet 4.5 is documented with image input; DeepSeek V4 Pro 0813 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Sonnet 4.5 or DeepSeek V4 Pro 0813?+
Neither has a larger sourced maximum output. Claude Sonnet 4.5 is 64K and DeepSeek V4 Pro 0813 is —.
Do Claude Sonnet 4.5 and DeepSeek V4 Pro 0813 support reasoning and tool use?+
Claude Sonnet 4.5: reasoning, tool calling, and image input. DeepSeek V4 Pro 0813: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Sonnet 4.5 or DeepSeek V4 Pro 0813?+
Claude Sonnet 4.5 has 1 sourced provider route; DeepSeek V4 Pro 0813 has 3, so DeepSeek V4 Pro 0813 has broader tracked availability.
Which offers better value, Claude Sonnet 4.5 or DeepSeek V4 Pro 0813?+
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.