Coding
Like-for-like- DeepSeek V3.2
- 60.9
- Gemma 4 31B
- 41.6
- Weighted basis
- 1 vs 1 rows
- Reading
- DeepSeek V3.2 leads
DeepSeek V3.2 vs Gemma 4 31B
Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemma 4 31B has the higher public score estimate, 60.15 versus 54.67, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 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.
Coding work
Code generation, repair, and software-engineering tasks
DeepSeek V3.2
DeepSeek V3.2 leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemma 4 31B
Gemma 4 31B has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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. Gemma 4 31B has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 DeepSeek V3.2 only
3 Shared
5 Gemma 4 31B only
- Shared results
- 3
- DeepSeek V3.2 only
- 4
- Gemma 4 31B only
- 5
- Like-for-like categories
- 1 / 8
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
- Gemma 4 31B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3.2
- Not measured
- Gemma 4 31B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3.2
- Not measured
- Gemma 4 31B
- 52.9
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3.2
- 17.1
- Gemma 4 31B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3.2
- Not measured
- Gemma 4 31B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3.2
- Not measured
- Gemma 4 31B
- 76.9
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3.2
- Not measured
- Gemma 4 31B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | DeepSeek V3.2 | Gemma 4 31B | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 60.9 | 41.6 | Like-for-like1 vs 1 rows | DeepSeek V3.2 leads |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 52.9 | Not comparable0 vs 3 rows | Not comparable |
| Math | 17.1 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 76.9 | Not comparable0 vs 1 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.
SWE-Rebench
Coding
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
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
Gemma 4 31B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V3.2
- $0.01526
- Fits in one request
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
Gemma 4 31B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V3.2
- $0.0154
- Does not fit in one request
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
DeepSeek V3.2 does not fit this workload in one request. Gemma 4 31B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V3.2Gemma 4 31B
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3.2
128K
Gemma 4 31B
API model ID
DeepSeek V3.2
Not sourced
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideCached-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
Gemma 4 31B
No comparable hosted API rate
Documented inputs
DeepSeek V3.2
Not sourced
Gemma 4 31B
text, image
Google Gemma 4 model documentationDocumented outputs
DeepSeek V3.2
Not sourced
Gemma 4 31B
Provider availability
DeepSeek V3.2
Not sourced
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideReasoning profile
DeepSeek V3.2
Non-Reasoning
Gemma 4 31B
Reasoning
Weight access
DeepSeek V3.2
Open Weight
Gemma 4 31B
Open Weight
License
DeepSeek V3.2
Open Weight
Gemma 4 31B
Open Weight
Release date
DeepSeek V3.2
2025-12-01
Gemma 4 31B
2026-04-02
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
- Gemma 4 31B has the higher public score estimate, 60.15 versus 54.67, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Gemma 4 31B has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek V3.2
API / mo$525
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence12 rows
Agentic
Claw-Eval
DeepSeek V3.240.2%SourceGemma 4 31B—Not directly comparable
VITA-Bench
DeepSeek V3.218.5%SourceGemma 4 31B—Not directly comparable
Gert Labs
Shared sourceDeepSeek V3.229.57%Gemma 4 31B35.26%Gemma 4 31B leads this result
Coding
SWE-Rebench
Shared sourceDeepSeek V3.260.9%Gemma 4 31B41.6%DeepSeek V3.2 leads this result
React Native Evals
Shared sourceDeepSeek V3.271.5%Gemma 4 31B75.2%Gemma 4 31B leads this result
Knowledge
Math
Multimodal
MMMU-Pro
DeepSeek V3.2—Gemma 4 31B76.9%SourceNot directly comparable
Frequently asked questions
Which is better, DeepSeek V3.2 or Gemma 4 31B?
Gemma 4 31B has the higher public score estimate, 60.15 versus 54.67, 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 Gemma 4 31B?
DeepSeek V3.2 leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, DeepSeek V3.2 or Gemma 4 31B?
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 Gemma 4 31B?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, DeepSeek V3.2 or Gemma 4 31B?
Gemma 4 31B has the larger documented context window: 256K, compared with 128K.
Related comparisons
Last updated August 18, 2026
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