Agentic
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- 59.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Model comparison
Claude Opus 4.6 (Adaptive) vs DeepSeek V4 Pro
Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Claude Opus 4.6 (Adaptive) only
23 DeepSeek V4 Pro only
- Shared results
- 0
- Claude Opus 4.6 (Adaptive) only
- 1
- DeepSeek V4 Pro only
- 23
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- 65.3
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- 41.3
- Weighted basis
- 0 vs 4 rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- 31.7
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.6 (Adaptive)
- Not measured
- DeepSeek V4 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Opus 4.6 (Adaptive) | DeepSeek V4 Pro | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 59.1 | Not comparable0 vs 1 rows | Not comparable |
| Coding | Not measured | 65.3 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 41.3 | Not comparable0 vs 4 rows | Not comparable |
| Math | Not measured | 31.7 | Not comparable0 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Claude Opus 4.6 (Adaptive)
- API rate not published
- Fits in one request
- DeepSeek V4 Pro
- $0.00087
- Fits in one request
Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.6 (Adaptive)
- API rate not published
- Fits in one request
- DeepSeek V4 Pro
- $0.02436
- Fits in one request
Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.6 (Adaptive)
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- DeepSeek V4 Pro
- $0.01812
- Fits in one request
Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.6 (Adaptive)DeepSeek V4 Pro
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6 (Adaptive)
DeepSeek V4 Pro
API model ID
Claude Opus 4.6 (Adaptive)
claude-opus-4-6
Anthropic model ID documentationDeepSeek V4 Pro
deepseek-v4-pro
DeepSeek models and pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6 (Adaptive)
No comparable hosted API rate
DeepSeek V4 Pro
$0.003625 per 1M cached input tokens
Documented inputs
Claude Opus 4.6 (Adaptive)
text, image
Anthropic model overviewDeepSeek V4 Pro
Documented outputs
Claude Opus 4.6 (Adaptive)
DeepSeek V4 Pro
Provider availability
Claude Opus 4.6 (Adaptive)
Generally Available · Claude API
Anthropic model overviewDeepSeek V4 Pro
Preview · DeepSeek API, open weights
DeepSeek V4 Flash 0731 updateReasoning profile
Claude Opus 4.6 (Adaptive)
Reasoning
DeepSeek V4 Pro
Non-Reasoning
Weight access
Claude Opus 4.6 (Adaptive)
Proprietary
DeepSeek V4 Pro
Open Weight
License
Claude Opus 4.6 (Adaptive)
Proprietary
DeepSeek V4 Pro
Open Weight
Release date
Claude Opus 4.6 (Adaptive)
2026-02-01
DeepSeek V4 Pro
2026-04-24
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence24 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro59.1%SourceNot directly comparable
MCP Atlas
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro69.4%SourceNot directly comparable
Toolathlon
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro46.3%SourceNot directly comparable
Claw-Eval
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro59.8%SourceNot directly comparable
Gert Labs
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro50.28%SourceNot directly comparable
ResearchClawBench
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro17.1%SourceNot directly comparable
Coding
Vibe Code Bench
Claude Opus 4.6 (Adaptive)53.50%SourceDeepSeek V4 Pro—Not directly comparable
LiveCodeBench Pass@1-COT
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro56.8%SourceNot directly comparable
SWE-bench Verified
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro73.6%SourceNot directly comparable
SWE-bench Pro
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro52.1%SourceNot directly comparable
SWE Multilingual
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro69.8%SourceNot directly comparable
Terminal-Bench 2.0
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro59.1%SourceNot directly comparable
Reasoning
Knowledge
MMLU-Pro
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro82.9%SourceNot directly comparable
SimpleQA
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro45%SourceNot directly comparable
Chinese-SimpleQA
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro75.8%SourceNot directly comparable
GPQA
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro72.9%SourceNot directly comparable
GPQA-D
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro72.9%SourceNot directly comparable
HLE
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro7.7%SourceNot directly comparable
Math
HMMT Feb 2026
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro31.7%SourceNot directly comparable
IMOAnswerBench
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro35.3%SourceNot directly comparable
Apex
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro0.4%SourceNot directly comparable
Apex Shortlist
Claude Opus 4.6 (Adaptive)—DeepSeek V4 Pro9.2%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Opus 4.6 (Adaptive) or DeepSeek V4 Pro?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Opus 4.6 (Adaptive) or DeepSeek V4 Pro?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Claude Opus 4.6 (Adaptive) or DeepSeek V4 Pro?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Claude Opus 4.6 (Adaptive) or DeepSeek V4 Pro?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Claude Opus 4.6 (Adaptive) or DeepSeek V4 Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated August 11, 2026
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