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Prompts that approach the documented context limit
Claude Opus 4.7
Claude Opus 4.7 has the larger documented context window.
Confidence: documented
Claude Opus 4.7 vs DeepSeek V3
Updated October 13, 2026. Rank says Claude Opus 4.7 is ahead. Price, access, and your workload can each overturn that. 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
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Decision reading
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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Claude Opus 4.7 only
- 15
- DeepSeek V3 only
- 9
- Like-for-like categories
- 0 / 8
Estimated: Claude Opus 4.7 · Supported: DeepSeek V3. Conditional ranges do not establish rank confidence.How the comparison works
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 V3
DeepSeek V3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek V3
DeepSeek V3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V3 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3 does not fit this workload in one request. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
55.6Claude Opus 4.715.1DeepSeek V3
Directional only · BenchAlign v5.8
Claude Opus 4.7 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
15 Claude Opus 4.7 only
9 DeepSeek V3 only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Agentic
Directional only- Claude Opus 4.7
- 54.0
- Supported · #40/124
- DeepSeek V3
- 10.0
- Estimated · #118/124
- Basis
- BenchAlign v5.8 lane · 5 vs 0 public rows
- Reading
- Directional only
Coding
Directional only- Claude Opus 4.7
- 55.6
- Supported · #30/149
- DeepSeek V3
- 15.1
- Estimated · #134/149
- Basis
- BenchAlign v5.8 lane · 6 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Opus 4.7
- 65.3
- Estimated · #30/181
- DeepSeek V3
- 32.3
- Supported · #143/181
- Basis
- BenchAlign v5.8 lane · 2 vs 3 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.7
- Not ranked
- DeepSeek V3
- 26.3
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7
- Not ranked
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7
- Not ranked
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7
- Not ranked
- DeepSeek V3
- 39.6
- #105/128
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7
- 72.9
- Unranked · 2 rankable rows
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 | DeepSeek V3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 54.0Supported · #40/124 | 10.0Estimated · #118/124 | Directional onlyBenchAlign v5.8 lane · 5 vs 0 public rows | Directional only |
| Coding | 55.6Supported · #30/149 | 15.1Estimated · #134/149 | Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows | Directional only |
| Knowledge | 65.3Estimated · #30/181 | 32.3Supported · #143/181 | Directional onlyBenchAlign v5.8 lane · 2 vs 3 public rows | Directional only |
| Reasoning | Not ranked | 26.3Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 39.6#105/128 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 72.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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 4.7
- $0.0175
- Fits in one request
- DeepSeek V3
- $0.00082
- Fits in one request
DeepSeek V3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7
- $0.325
- Fits in one request
- DeepSeek V3
- $0.0168
- Fits in one request
DeepSeek V3 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 4.7
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- DeepSeek V3
- $0.0304
- Does not fit in one request
DeepSeek V3 does not fit this workload in one request. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.7DeepSeek V3
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7
DeepSeek V3
128K
API model ID
Claude Opus 4.7
claude-opus-4-7
Anthropic model ID documentationDeepSeek V3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.7
Not published
DeepSeek V3
$0.07 per 1M cached input tokens
Documented inputs
Claude Opus 4.7
text, image
Anthropic model overviewDeepSeek V3
Not sourced
Documented outputs
Claude Opus 4.7
DeepSeek V3
Not sourced
Provider availability
Claude Opus 4.7
Generally Available · Claude API
Anthropic model overviewDeepSeek V3
Not sourced
Reasoning profile
Claude Opus 4.7
Non-Reasoning
DeepSeek V3
Non-Reasoning
Weight access
Claude Opus 4.7
Proprietary
DeepSeek V3
Open Weight
License
Claude Opus 4.7
Proprietary
DeepSeek V3
Open Weight
Release date
Claude Opus 4.7
2026-04-16
DeepSeek V3
2024-12-26
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
- Repository review: $0.325 vs $0.0168. Cache-heavy agent loop: $1.35 vs $0.0304.
- Context tradeoff
- Claude Opus 4.7 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 4.7 or DeepSeek V3?
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 4.7 or DeepSeek V3?
Claude Opus 4.7 scores higher for coding on the public lane, 55.6 to 15.1. DeepSeek V3 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude Opus 4.7 or DeepSeek V3?
Claude Opus 4.7 scores higher for agentic tasks on the public lane, 54 to 10. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Claude Opus 4.7 or DeepSeek V3?
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.00082 on DeepSeek V3; repository review costs $0.325 and $0.0168; the cache-heavy agent loop costs $1.35 and $0.0304. DeepSeek V3 does not fit this workload in one request. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.7 or DeepSeek V3?
Claude Opus 4.7 has the larger documented context window: 1M, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Claude Opus 4.7
API / mo$22,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
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
Gert Labs
Claude Opus 4.765.59%SourceDeepSeek V3—Not directly comparable
ResearchClawBench
Claude Opus 4.720.7%SourceDeepSeek V3—Not directly comparable
OSWorld 2.0
Claude Opus 4.713.9%SourceDeepSeek V3—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.768.5%SourceDeepSeek V3—Not directly comparable
ApprenticeBench
Claude Opus 4.77%SourceDeepSeek V3—Not directly comparable
Coding
Vibe Code Bench
Claude Opus 4.771.00%SourceDeepSeek V3—Not directly comparable
React Native Evals
Claude Opus 4.782.8%SourceDeepSeek V3—Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.738.5%SourceDeepSeek V3—Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.785.1%SourceDeepSeek V3—Not directly comparable
SWE-bench (Vals)
Claude Opus 4.782.0%SourceDeepSeek V3—Not directly comparable
PostTrainBench v1.1
Claude Opus 4.728.6%SourceDeepSeek V3—Not directly comparable
LiveCodeBench
Claude Opus 4.7—DeepSeek V337.6%SourceNot directly comparable
SWE-bench Verified
Claude Opus 4.7—DeepSeek V342%SourceNot directly comparable
Reasoning
LongBench v2
Claude Opus 4.7—DeepSeek V348.7%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Claude Opus 4.790.2%SourceDeepSeek V3—Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.789.9%SourceDeepSeek V3—Not directly comparable
GPQA
Claude Opus 4.7—DeepSeek V359.1%SourceNot directly comparable
MMLU-Pro
Claude Opus 4.7—DeepSeek V375.9%SourceNot directly comparable
SimpleQA
Claude Opus 4.7—DeepSeek V324.9%SourceNot directly comparable
Instruction following
IFEval
Claude Opus 4.7—DeepSeek V386.1%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.770.175%SourceDeepSeek V3—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.731.707%SourceDeepSeek V3—Not directly comparable
AIME 2024
Claude Opus 4.7—DeepSeek V339.2%SourceNot directly comparable
MATH-500
Claude Opus 4.7—DeepSeek V390.2%SourceNot directly comparable
24 public results · 0 shared
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Last updated October 13, 2026