Math
Directional only- DeepSeek V3.2
- 17.1
- GPT-4.1 nano
- 1.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
DeepSeek V3.2 vs GPT-4.1 nano
Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
DeepSeek V3.2 has the higher public score estimate, 54.5 versus 41.1, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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.
Long documents
Prompts that approach the documented context limit
GPT-4.1 nano
GPT-4.1 nano has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-4.1 nano
GPT-4.1 nano 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
GPT-4.1 nano
GPT-4.1 nano 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
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
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. DeepSeek V3.2 does not fit this workload in one request. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 DeepSeek V3.2 only
1 Shared
3 GPT-4.1 nano only
- Shared results
- 1
- DeepSeek V3.2 only
- 6
- GPT-4.1 nano only
- 3
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4.1 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V3.2
- 60.9
- GPT-4.1 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4.1 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4.1 nano
- 50.3
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4.1 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4.1 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4.1 nano
- 83.2
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | DeepSeek V3.2 | GPT-4.1 nano | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 17.1 | 1.0 | Directional only2 vs 1 rows | Directional only |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | 60.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 50.3 | 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 | 83.2 | Not comparable0 vs 1 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.
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.
FrontierMath v2 (Tiers 1-3)
Math
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
- DeepSeek V3.2
- $0.00049
- Fits in one request
- GPT-4.1 nano
- $0.0003
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V3.2
- $0.01526
- Fits in one request
- GPT-4.1 nano
- $0.0062
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V3.2
- $0.0154
- Does not fit in one request
- GPT-4.1 nano
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
DeepSeek V3.2 does not fit this workload in one request. GPT-4.1 nano 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.
SpecificationDeepSeek V3.2GPT-4.1 nano
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3.2
128K
GPT-4.1 nano
1M
API model ID
DeepSeek V3.2
Not sourced
GPT-4.1 nano
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V3.2
$0.028 per 1M cached input tokens
GPT-4.1 nano
Not published
Documented inputs
DeepSeek V3.2
Not sourced
GPT-4.1 nano
Not sourced
Documented outputs
DeepSeek V3.2
Not sourced
GPT-4.1 nano
Not sourced
Provider availability
DeepSeek V3.2
Not sourced
GPT-4.1 nano
Not sourced
Reasoning profile
DeepSeek V3.2
Non-Reasoning
GPT-4.1 nano
Non-Reasoning
Weight access
DeepSeek V3.2
Open Weight
GPT-4.1 nano
Proprietary
License
DeepSeek V3.2
Open Weight
GPT-4.1 nano
Proprietary
Release date
DeepSeek V3.2
2025-12-01
GPT-4.1 nano
2025-04-14
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
- DeepSeek V3.2 has the higher public score estimate, 54.5 versus 41.1, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.01526 vs $0.0062. Cache-heavy agent loop: $0.0154 vs $0.026.
- Context tradeoff
- GPT-4.1 nano has the larger documented window (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 evidence10 rows
Agentic
Coding
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceDeepSeek V3.222.100%GPT-4.1 nano1.034%DeepSeek V3.2 leads this result
FrontierMath v2 (Tier 4)
DeepSeek V3.22.100%SourceGPT-4.1 nano—Not directly comparable
Instruction following
IFEval
DeepSeek V3.2—GPT-4.1 nano83.2%SourceNot directly comparable
Frequently asked questions
Which is better, DeepSeek V3.2 or GPT-4.1 nano?
DeepSeek V3.2 has the higher public score estimate, 54.5 versus 41.1, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, DeepSeek V3.2 or GPT-4.1 nano?
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, DeepSeek V3.2 or GPT-4.1 nano?
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, DeepSeek V3.2 or GPT-4.1 nano?
For the stated presets, chat costs $0.00049 on DeepSeek V3.2 and $0.0003 on GPT-4.1 nano; repository review costs $0.01526 and $0.0062; the cache-heavy agent loop costs $0.0154 and $0.026. DeepSeek V3.2 does not fit this workload in one request. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, DeepSeek V3.2 or GPT-4.1 nano?
GPT-4.1 nano has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated July 29, 2026
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