Agentic
Not comparable- Claude Opus 4.6
- 50.1
- Supported · #58/154
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- BenchAlign lane · 10 vs 11 public rows
- Reading
- Not comparable
Claude Opus 4.6 vs DeepSeek V4 Flash 0731
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 21, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
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.
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V4 Flash 0731 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V4 Flash 0731 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
56.8Claude Opus 4.6—DeepSeek V4 Flash 0731
Not comparable · BenchAlign
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
25 Claude Opus 4.6 only
9 Shared
31 DeepSeek V4 Flash 0731 only
- Shared results
- 9
- Claude Opus 4.6 only
- 25
- DeepSeek V4 Flash 0731 only
- 31
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- Claude Opus 4.6
- 56.8
- Supported · #35/156
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- BenchAlign lane · 8 vs 15 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 4.6
- 67.1
- Unranked · 2 rankable rows
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.6
- 59.6
- #29/49
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.6
- 61.2
- Estimated · #34/186
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- BenchAlign lane · 9 vs 8 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.6
- Not ranked
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.6
- 51.0
- #79/124
- DeepSeek V4 Flash 0731
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.6
- 58.5
- Unranked · 3 rankable rows
- DeepSeek V4 Flash 0731
- 79.9
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.6 | DeepSeek V4 Flash 0731 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.1Supported · #58/154 | Not ranked | Not comparableBenchAlign lane · 10 vs 11 public rows | Not comparable |
| Coding | 56.8Supported · #35/156 | Not ranked | Not comparableBenchAlign lane · 8 vs 15 public rows | Not comparable |
| Reasoning | 67.1Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 59.6#29/49 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 61.2Estimated · #34/186 | Not ranked | Not comparableBenchAlign lane · 9 vs 8 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 51.0#79/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 58.5Unranked · 3 rankable rows | 79.9Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
HLE
Knowledge
Claude Opus 4.6: 53%DeepSeek V4 Flash 0731: 34.8%Normalized gap 18.2Claude Opus 4.6 source DeepSeek V4 Flash 0731 sourceBrowseComp
Agentic
Claude Opus 4.6: 83.7%DeepSeek V4 Flash 0731: 73.2%Normalized gap 10.5Claude Opus 4.6 source DeepSeek V4 Flash 0731 sourceTerminal-Bench 2.0
Agentic
Claude Opus 4.6: 65.4%DeepSeek V4 Flash 0731: 56.9%Normalized gap 8.5Claude Opus 4.6 source DeepSeek V4 Flash 0731 sourceMMLU-Pro
Knowledge
Claude Opus 4.6: 82%DeepSeek V4 Flash 0731: 86.2%Normalized gap 4.2Claude Opus 4.6 source DeepSeek V4 Flash 0731 sourceGPQA
Knowledge
Claude Opus 4.6: 91.3%DeepSeek V4 Flash 0731: 88.1%Normalized gap 3.2Claude Opus 4.6 source DeepSeek V4 Flash 0731 source
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
- $0.0175
- Fits in one request
- DeepSeek V4 Flash 0731
- $0.00028
- Fits in one request
DeepSeek V4 Flash 0731 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.6
- $0.325
- Fits in one request
- DeepSeek V4 Flash 0731
- $0.00784
- Fits in one request
DeepSeek V4 Flash 0731 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.6
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- DeepSeek V4 Flash 0731
- $0.00616
- Fits in one request
DeepSeek V4 Flash 0731 has the lower modeled cost
Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.6DeepSeek V4 Flash 0731
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6
1M
DeepSeek V4 Flash 0731
API model ID
Claude Opus 4.6
Not sourced
DeepSeek V4 Flash 0731
deepseek-v4-flash
DeepSeek-V4.1-Flash releaseCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6
Not published
DeepSeek V4 Flash 0731
$0.0028 per 1M cached input tokens
Documented inputs
Claude Opus 4.6
Not sourced
DeepSeek V4 Flash 0731
Documented outputs
Claude Opus 4.6
Not sourced
DeepSeek V4 Flash 0731
Provider availability
Claude Opus 4.6
Not sourced
DeepSeek V4 Flash 0731
Deprecated · DeepSeek API
DeepSeek-V4.1-Flash releaseReasoning profile
Claude Opus 4.6
Non-Reasoning
DeepSeek V4 Flash 0731
Reasoning
Weight access
Claude Opus 4.6
Proprietary
DeepSeek V4 Flash 0731
Proprietary
License
Claude Opus 4.6
Proprietary
DeepSeek V4 Flash 0731
Proprietary
Release date
Claude Opus 4.6
2026-02-01
DeepSeek V4 Flash 0731
2026-07-31
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.325 vs $0.00784. Cache-heavy agent loop: $1.35 vs $0.00616.
- 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 evidence65 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.665.4%SourceDeepSeek V4 Flash 073156.9%SourceClaude Opus 4.6 leads this result
BrowseComp
Claude Opus 4.683.7%SourceDeepSeek V4 Flash 073173.2%SourceClaude Opus 4.6 leads this result
OSWorld-Verified
Claude Opus 4.672.7%SourceDeepSeek V4 Flash 0731—Not directly comparable
Claw-Eval
Claude Opus 4.670.4%SourceDeepSeek V4 Flash 0731—Not directly comparable
DeepSearchQA
Claude Opus 4.673.7%SourceDeepSeek V4 Flash 0731—Not directly comparable
CyberGym
Claude Opus 4.666.6%SourceDeepSeek V4 Flash 073176.7%SourceDeepSeek V4 Flash 0731 leads this result
Gert Labs
Claude Opus 4.661.85%SourceDeepSeek V4 Flash 0731—Not directly comparable
ResearchClawBench
Claude Opus 4.619.9%SourceDeepSeek V4 Flash 0731—Not directly comparable
JobBench
Claude Opus 4.636.7%SourceDeepSeek V4 Flash 0731—Not directly comparable
ApprenticeBench
Claude Opus 4.65%SourceDeepSeek V4 Flash 0731—Not directly comparable
HLE w/ tools
Claude Opus 4.6—DeepSeek V4 Flash 073145.1%SourceNot directly comparable
MCP Atlas
Claude Opus 4.6—DeepSeek V4 Flash 073169%SourceNot directly comparable
Toolathlon
Claude Opus 4.6—DeepSeek V4 Flash 073147.8%SourceNot directly comparable
Terminal-Bench 2.1
Claude Opus 4.6—DeepSeek V4 Flash 073182.7%SourceNot directly comparable
Toolathlon-Verified
Claude Opus 4.6—DeepSeek V4 Flash 073170.3%SourceNot directly comparable
Agents' Last Exam
Claude Opus 4.6—DeepSeek V4 Flash 073125.2%SourceNot directly comparable
AutomationBench
Claude Opus 4.6—DeepSeek V4 Flash 073125.1%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6—DeepSeek V4 Flash 073167.0%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.680.8%SourceDeepSeek V4 Flash 073179%SourceClaude Opus 4.6 leads this result
SWE-bench Verified*
Claude Opus 4.675.6%SourceDeepSeek V4 Flash 0731—Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%SourceDeepSeek V4 Flash 0731—Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%SourceDeepSeek V4 Flash 073152.6%SourceClaude Opus 4.6 leads this result
SWE-Rebench
Claude Opus 4.665.3%SourceDeepSeek V4 Flash 0731—Not directly comparable
React Native Evals
Claude Opus 4.684.1%SourceDeepSeek V4 Flash 0731—Not directly comparable
Vibe Code Bench
Claude Opus 4.657.57%SourceDeepSeek V4 Flash 0731—Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.626.9%SourceDeepSeek V4 Flash 0731—Not directly comparable
LiveCodeBench Pass@1-COT
Claude Opus 4.6—DeepSeek V4 Flash 073191.6%SourceNot directly comparable
Codeforces
Claude Opus 4.6—DeepSeek V4 Flash 07313052.0SourceNot directly comparable
SWE Multilingual
Claude Opus 4.6—DeepSeek V4 Flash 073173.3%SourceNot directly comparable
Terminal-Bench 2.0
Claude Opus 4.6—DeepSeek V4 Flash 073156.9%SourceNot directly comparable
Terminal-Bench 2.1
Claude Opus 4.6—DeepSeek V4 Flash 073182.7%SourceNot directly comparable
NL2Repo
Claude Opus 4.6—DeepSeek V4 Flash 073154.2%SourceNot directly comparable
DeepSWE
Claude Opus 4.6—DeepSeek V4 Flash 073154.4%SourceNot directly comparable
DSBench-FullStack
Claude Opus 4.6—DeepSeek V4 Flash 073168.7%SourceNot directly comparable
DSBench-Hard
Claude Opus 4.6—DeepSeek V4 Flash 073159.6%SourceNot directly comparable
VulcanBench v3
Claude Opus 4.6—DeepSeek V4 Flash 073188.4%SourceNot directly comparable
OpenHarmony Bench
Claude Opus 4.6—DeepSeek V4 Flash 073153.8%SourceNot directly comparable
LiveCodeBench (Vals)
Claude Opus 4.6—DeepSeek V4 Flash 073187.3%SourceNot directly comparable
SWE-bench (Vals)
Claude Opus 4.6—DeepSeek V4 Flash 073188.8%SourceNot directly comparable
Reasoning
Multimodal
MMMU-Pro
Claude Opus 4.677.3%SourceDeepSeek V4 Flash 0731—Not directly comparable
ERQA
Claude Opus 4.651.6%SourceDeepSeek V4 Flash 0731—Not directly comparable
ScreenSpot Pro
Claude Opus 4.683.1%SourceDeepSeek V4 Flash 0731—Not directly comparable
MedXpertQA (MM)
Claude Opus 4.664.8%SourceDeepSeek V4 Flash 0731—Not directly comparable
Knowledge
GPQA
Claude Opus 4.691.3%SourceDeepSeek V4 Flash 073188.1%SourceClaude Opus 4.6 leads this result
GPQA-D
Claude Opus 4.689.2%SourceDeepSeek V4 Flash 073188.1%SourceClaude Opus 4.6 leads this result
SuperGPQA
Claude Opus 4.695%SourceDeepSeek V4 Flash 0731—Not directly comparable
MMLU-Pro
Claude Opus 4.682%SourceDeepSeek V4 Flash 073186.2%SourceDeepSeek V4 Flash 0731 leads this result
MMLU-Pro (Arcee)
Claude Opus 4.689.1%SourceDeepSeek V4 Flash 0731—Not directly comparable
HLE
Claude Opus 4.653%SourceDeepSeek V4 Flash 073134.8%SourceClaude Opus 4.6 leads this result
HLE w/o tools
Claude Opus 4.640%SourceDeepSeek V4 Flash 0731—Not directly comparable
HealthBench Hard
Claude Opus 4.614.8%SourceDeepSeek V4 Flash 0731—Not directly comparable
MedXpertQA (Text)
Claude Opus 4.652.1%SourceDeepSeek V4 Flash 0731—Not directly comparable
SimpleQA
Claude Opus 4.6—DeepSeek V4 Flash 073134.1%SourceNot directly comparable
Chinese-SimpleQA
Claude Opus 4.6—DeepSeek V4 Flash 073178.9%SourceNot directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6—DeepSeek V4 Flash 073189.9%SourceNot directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6—DeepSeek V4 Flash 073186.2%SourceNot directly comparable
Math
AIME25 (Arcee)
Claude Opus 4.699.8%SourceDeepSeek V4 Flash 0731—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.640.700%SourceDeepSeek V4 Flash 0731—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.622.900%SourceDeepSeek V4 Flash 0731—Not directly comparable
HMMT Feb 2026
Claude Opus 4.6—DeepSeek V4 Flash 073194.8%SourceNot directly comparable
IMOAnswerBench
Claude Opus 4.6—DeepSeek V4 Flash 073188.4%SourceNot directly comparable
Apex
Claude Opus 4.6—DeepSeek V4 Flash 073133.0%SourceNot directly comparable
Apex Shortlist
Claude Opus 4.6—DeepSeek V4 Flash 073185.7%SourceNot directly comparable
Questions
Which is better, Claude Opus 4.6 or DeepSeek V4 Flash 0731?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Opus 4.6 or DeepSeek V4 Flash 0731?
DeepSeek V4 Flash 0731 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Opus 4.6 or DeepSeek V4 Flash 0731?
DeepSeek V4 Flash 0731 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Opus 4.6 or DeepSeek V4 Flash 0731?
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.00028 on DeepSeek V4 Flash 0731; repository review costs $0.325 and $0.00784; the cache-heavy agent loop costs $1.35 and $0.00616. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.6 or DeepSeek V4 Flash 0731?
Both models list the same context window, 1M.
Related comparisons
Last updated September 21, 2026
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