Coding
Directional only- Gemma 4 31B
- 41.6
- GPT-5.3 Codex
- 67.2
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
Gemma 4 31B vs GPT-5.3 Codex
Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.3 Codex has the higher public score estimate, 65.93 versus 60.15, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 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-5.3 Codex
GPT-5.3 Codex has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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
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
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
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.
6 Gemma 4 31B only
2 Shared
6 GPT-5.3 Codex only
- Shared results
- 2
- Gemma 4 31B only
- 6
- GPT-5.3 Codex only
- 6
- 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- Gemma 4 31B
- Not measured
- GPT-5.3 Codex
- 71.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemma 4 31B
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemma 4 31B
- 52.9
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 31B
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 31B
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 31B
- 76.9
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemma 4 31B
- Not measured
- GPT-5.3 Codex
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemma 4 31B | GPT-5.3 Codex | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 41.6 | 67.2 | Directional only1 vs 3 rows | Directional only |
| Agentic | Not measured | 71.4 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 52.9 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 76.9 | Not measured | Not comparable1 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.
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
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.3 Codex
- $0.00875
- Fits in one request
Gemma 4 31B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.3 Codex
- $0.1295
- 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
- Gemma 4 31B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- GPT-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. 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.
SpecificationGemma 4 31BGPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 31B
GPT-5.3 Codex
API model ID
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideGPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 31B
No comparable hosted API rate
GPT-5.3 Codex
Not published
Documented inputs
Gemma 4 31B
text, image
Google Gemma 4 model documentationGPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
Gemma 4 31B
GPT-5.3 Codex
Provider availability
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideGPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemma 4 31B
Reasoning
GPT-5.3 Codex
Reasoning
Weight access
Gemma 4 31B
Open Weight
GPT-5.3 Codex
Proprietary
License
Gemma 4 31B
Open Weight
GPT-5.3 Codex
Proprietary
Release date
Gemma 4 31B
2026-04-02
GPT-5.3 Codex
2026-02-05
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
- GPT-5.3 Codex has the higher public score estimate, 65.93 versus 60.15, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.3 Codex has the larger documented window (400K).
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.
Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
GPT-5.3 Codex
API / mo$11,813
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence14 rows
Agentic
Gert Labs
Shared sourceGemma 4 31B35.26%GPT-5.3 Codex57.47%GPT-5.3 Codex leads this result
Terminal-Bench 2.0
Gemma 4 31B—GPT-5.3 Codex77.3%SourceNot directly comparable
OSWorld-Verified
Gemma 4 31B—GPT-5.3 Codex64.7%SourceNot directly comparable
JobBench
Gemma 4 31B—GPT-5.3 Codex33.7%SourceNot directly comparable
Coding
SWE-Rebench
Gemma 4 31B41.6%SourceGPT-5.3 Codex58.2%SourceGPT-5.3 Codex leads this result
React Native Evals
Gemma 4 31B75.2%SourceGPT-5.3 Codex—Not directly comparable
SWE-bench Verified
Gemma 4 31B—GPT-5.3 Codex85%SourceNot directly comparable
SWE-bench Pro
Gemma 4 31B—GPT-5.3 Codex56.8%SourceNot directly comparable
Vibe Code Bench
Gemma 4 31B—GPT-5.3 Codex61.77%SourceNot directly comparable
Knowledge
Multimodal
MMMU-Pro
Gemma 4 31B76.9%SourceGPT-5.3 Codex—Not directly comparable
Frequently asked questions
Which is better, Gemma 4 31B or GPT-5.3 Codex?
GPT-5.3 Codex has the higher public score estimate, 65.93 versus 60.15, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemma 4 31B or GPT-5.3 Codex?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemma 4 31B or GPT-5.3 Codex?
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, Gemma 4 31B or GPT-5.3 Codex?
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, Gemma 4 31B or GPT-5.3 Codex?
GPT-5.3 Codex has the larger documented context window: 400K, compared with 256K.
Related comparisons
Last updated August 18, 2026
Watch Gemma 4 31B vs GPT-5.3 Codex
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.