Agentic
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- BenchAlign v5.6 lane · 1 vs 0 public rows
- Reading
- Not comparable
Claude 4.1 Opus vs DeepSeek V3.2 (Thinking)
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.
Updated September 22, 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.
Long documents
Prompts that approach the documented context limit
Claude 4.1 Opus
Claude 4.1 Opus has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V3.2 (Thinking)
DeepSeek V3.2 (Thinking) 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 V3.2 (Thinking)
DeepSeek V3.2 (Thinking) 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
Claude 4.1 Opus 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
Claude 4.1 Opus and DeepSeek V3.2 (Thinking) are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Cache-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. Claude 4.1 Opus does not fit this workload in one request. DeepSeek V3.2 (Thinking) does not fit this workload in one request. Claude 4.1 Opus 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.
—Claude 4.1 Opus35.2DeepSeek V3.2 (Thinking)
Not comparable · BenchAlign v5.6
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.
2 Claude 4.1 Opus only
1 DeepSeek V3.2 (Thinking) only
- Shared results
- 0
- Claude 4.1 Opus only
- 2
- DeepSeek V3.2 (Thinking) only
- 1
- 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 v5.6 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 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- 35.2
- Estimated · #83/135
- Basis
- BenchAlign v5.6 lane · 1 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- BenchAlign v5.6 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude 4.1 Opus
- Not ranked
- DeepSeek V3.2 (Thinking)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude 4.1 Opus | DeepSeek V3.2 (Thinking) | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.6 lane · 1 vs 0 public rows | Not comparable |
| Coding | Not ranked | 35.2Estimated · #83/135 | Not comparableBenchAlign v5.6 lane · 1 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign v5.6 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) 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.
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 4.1 Opus
- $0.0525
- Fits in one request
- DeepSeek V3.2 (Thinking)
- $0.00165
- Fits in one request
DeepSeek V3.2 (Thinking) has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude 4.1 Opus
- $0.975
- Fits in one request
- DeepSeek V3.2 (Thinking)
- $0.03407
- Fits in one request
DeepSeek V3.2 (Thinking) 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 4.1 Opus
- $4.05
- Does not fit in one request
- Cached input priced at the published list-input rate
- DeepSeek V3.2 (Thinking)
- $0.0609
- Does not fit in one request
Claude 4.1 Opus does not fit this workload in one request. DeepSeek V3.2 (Thinking) does not fit this workload in one request. Claude 4.1 Opus 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 4.1 OpusDeepSeek V3.2 (Thinking)
Context window
Maximum documented context; output-token limits may be lower.
Claude 4.1 Opus
200K
DeepSeek V3.2 (Thinking)
128K
API model ID
Claude 4.1 Opus
Not sourced
DeepSeek V3.2 (Thinking)
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude 4.1 Opus
Not published
DeepSeek V3.2 (Thinking)
$0.14 per 1M cached input tokens
Documented inputs
Claude 4.1 Opus
Not sourced
DeepSeek V3.2 (Thinking)
Not sourced
Documented outputs
Claude 4.1 Opus
Not sourced
DeepSeek V3.2 (Thinking)
Not sourced
Provider availability
Claude 4.1 Opus
Not sourced
DeepSeek V3.2 (Thinking)
Not sourced
Reasoning profile
Claude 4.1 Opus
Non-Reasoning
DeepSeek V3.2 (Thinking)
Reasoning
Weight access
Claude 4.1 Opus
Proprietary
DeepSeek V3.2 (Thinking)
Open Weight
License
Claude 4.1 Opus
Proprietary
DeepSeek V3.2 (Thinking)
Open Weight
Release date
Claude 4.1 Opus
2025-08-01
DeepSeek V3.2 (Thinking)
2025-12-01
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: $0.975 vs $0.03407. Cache-heavy agent loop: $4.05 vs $0.0609.
- Context tradeoff
- Claude 4.1 Opus has the larger documented window (200K).
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 evidence3 rows
Questions
Which is better, Claude 4.1 Opus or DeepSeek V3.2 (Thinking)?
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 4.1 Opus or DeepSeek V3.2 (Thinking)?
Claude 4.1 Opus is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude 4.1 Opus or DeepSeek V3.2 (Thinking)?
Claude 4.1 Opus and DeepSeek V3.2 (Thinking) are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude 4.1 Opus or DeepSeek V3.2 (Thinking)?
For the stated presets, chat costs $0.0525 on Claude 4.1 Opus and $0.00164 on DeepSeek V3.2 (Thinking); repository review costs $0.975 and $0.03407; the cache-heavy agent loop costs $4.05 and $0.0609. Claude 4.1 Opus does not fit this workload in one request. DeepSeek V3.2 (Thinking) does not fit this workload in one request. Claude 4.1 Opus has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude 4.1 Opus or DeepSeek V3.2 (Thinking)?
Claude 4.1 Opus has the larger documented context window: 200K, compared with 128K.
Related comparisons
Last updated September 22, 2026
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