Math
Directional only- DeepSeek V3.2
- 17.1
- GPT-4o
- 0.3
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
DeepSeek V3.2 vs GPT-4o
Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
DeepSeek V3.2 has the higher public score estimate, 54.75 versus 41.19, 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.
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 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
DeepSeek V3.2 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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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-4o does not fit this workload in one request. GPT-4o 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
- Shared results
- 1
- DeepSeek V3.2 only
- 6
- GPT-4o only
- 0
- 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-4o
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V3.2
- 60.9
- GPT-4o
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4o
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4o
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4o
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4o
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3.2
- Not measured
- GPT-4o
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | DeepSeek V3.2 | GPT-4o | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 17.1 | 0.3 | 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 | Not measured | Not comparable0 vs 0 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.
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-4o
- $0.0075
- Fits in one request
DeepSeek V3.2 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-4o
- $0.155
- Fits in one request
DeepSeek V3.2 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-4o
- $0.65
- Does not fit in one request
- Cached input priced at the published list-input rate
DeepSeek V3.2 does not fit this workload in one request. GPT-4o does not fit this workload in one request. GPT-4o 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-4o
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3.2
128K
GPT-4o
128K
API model ID
DeepSeek V3.2
Not sourced
GPT-4o
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-4o
Not published
Documented inputs
DeepSeek V3.2
Not sourced
GPT-4o
Not sourced
Documented outputs
DeepSeek V3.2
Not sourced
GPT-4o
Not sourced
Provider availability
DeepSeek V3.2
Not sourced
GPT-4o
Not sourced
Reasoning profile
DeepSeek V3.2
Non-Reasoning
GPT-4o
Non-Reasoning
Weight access
DeepSeek V3.2
Open Weight
GPT-4o
Proprietary
License
DeepSeek V3.2
Open Weight
GPT-4o
Proprietary
Release date
DeepSeek V3.2
2025-12-01
GPT-4o
2024-05-13
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, 54.75 versus 41.19, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.01526 vs $0.155. Cache-heavy agent loop: $0.0154 vs $0.65.
- Context tradeoff
- Both models list 128K.
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 evidence7 rows
Agentic
Coding
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceDeepSeek V3.222.100%GPT-4o0.345%DeepSeek V3.2 leads this result
FrontierMath v2 (Tier 4)
DeepSeek V3.22.100%SourceGPT-4o—Not directly comparable
Frequently asked questions
Which is better, DeepSeek V3.2 or GPT-4o?
DeepSeek V3.2 has the higher public score estimate, 54.75 versus 41.19, 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-4o?
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-4o?
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-4o?
For the stated presets, chat costs $0.00049 on DeepSeek V3.2 and $0.0075 on GPT-4o; repository review costs $0.01526 and $0.155; the cache-heavy agent loop costs $0.0154 and $0.65. DeepSeek V3.2 does not fit this workload in one request. GPT-4o does not fit this workload in one request. GPT-4o 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-4o?
Both models list the same context window, 128K.
Related comparisons
Last updated September 3, 2026
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