Long documents
Prompts that approach the documented context limit
GPT-5.1-Codex
GPT-5.1-Codex has the larger documented context window.
Confidence: documented
Gemma 4 31B vs GPT-5.1-Codex
Updated November 13, 2026. Rank says GPT-5.1-Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
GPT-5.1-Codex has the higher public point estimate, 50.88 versus 28.81. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- API price per 1M tokens
Gemma 4 31B: No comparable API price
GPT-5.1-Codex: $1.25 input / $10 output
- Context window
Gemma 4 31B: 256K
GPT-5.1-Codex: 400K
- Released
Gemma 4 31B: April 2, 2026
GPT-5.1-Codex: November 13, 2025
- Shared results
- 1
- Gemma 4 31B only
- 12
- GPT-5.1-Codex only
- 3
- Like-for-like categories
- 0 / 8
Estimated: Gemma 4 31B and GPT-5.1-Codex. Conditional ranges do not establish rank confidence.How the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.1-Codex is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.1-Codex is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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: listed-ratesRepository 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
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.
28.8Gemma 4 31B36.3GPT-5.1-Codex
Directional only · BenchAlign v5.8
GPT-5.1-Codex has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
12 Gemma 4 31B only
1 Shared
3 GPT-5.1-Codex only
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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
Directional only- Gemma 4 31B
- 28.8
- Supported · #107/162
- GPT-5.1-Codex
- 36.3
- Estimated · #89/162
- Basis
- BenchAlign v5.8 lane · 3 vs 2 public rows
- Reading
- Directional only
Reasoning
Directional only- Gemma 4 31B
- 32.0
- Supported · #109/160
- GPT-5.1-Codex
- 45.2
- Estimated · #70/160
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemma 4 31B
- 36.7
- Supported · #121/180
- GPT-5.1-Codex
- 53.1
- Estimated · #76/180
- Basis
- BenchAlign v5.8 lane · 4 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 31B
- 92.9
- #16/157
- GPT-5.1-Codex
- 85.6
- #52/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemma 4 31B
- 12.5
- Estimated · #133/135
- GPT-5.1-Codex
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 2 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 31B
- 47.3
- Supported · #60/67
- GPT-5.1-Codex
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 31B
- Not ranked
- GPT-5.1-Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 31B
- Not ranked
- GPT-5.1-Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 31B | GPT-5.1-Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 28.8Supported · #107/162 | 36.3Estimated · #89/162 | Directional onlyBenchAlign v5.8 lane · 3 vs 2 public rows | Directional only |
| Reasoning | 32.0Supported · #109/160 | 45.2Estimated · #70/160 | Directional onlyBenchAlign v5.8 lane · 1 vs 0 public rows | Directional only |
| Knowledge | 36.7Supported · #121/180 | 53.1Estimated · #76/180 | Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows | Directional only |
| Instruction following | 92.9#16/157 | 85.6#52/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | 12.5Estimated · #133/135 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 2 public rows | Not comparable |
| Multimodal | 47.3Supported · #60/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Multilingual | 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, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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.
Supported evidence per lane · bars run 0–100Methodology
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-5.1-Codex
- $0.00625
- 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-5.1-Codex
- $0.0925
- 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-5.1-Codex
- $0.15
- Fits in one request
Gemma 4 31B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Price against score, among all priced models
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
GPT-5.1-Codex
- Gemma 4 31B has no public API price, so it is not plotted.
- 28 models score at least as high as GPT-5.1-Codex for the same price or less. The nearest is Gemini 4 Carbon (score 89.2, also $10.00 per 1M output tokens).
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See GPT-5.1-Codex on the full price vs performance chartSpecification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemma 4 31BGPT-5.1-Codex
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 31B
GPT-5.1-Codex
API model ID
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideGPT-5.1-Codex
gpt-5.1-codex
OpenAI GPT-5.1-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-5.1-Codex
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex model documentationDocumented inputs
Gemma 4 31B
text, image
Google Gemma 4 model documentationGPT-5.1-Codex
text, image
OpenAI model page for gpt-5.1-codexDocumented outputs
Gemma 4 31B
GPT-5.1-Codex
Provider availability
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideGPT-5.1-Codex
Not sourced
Reasoning profile
Gemma 4 31B
Reasoning
GPT-5.1-Codex
Reasoning
Weight access
Gemma 4 31B
Open Weight
GPT-5.1-Codex
Proprietary
License
Gemma 4 31B
Open Weight
GPT-5.1-Codex
Proprietary
Release date
Gemma 4 31B
2026-04-02
GPT-5.1-Codex
2025-11-13
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
- GPT-5.1-Codex has the higher public point estimate, 50.88 versus 28.81. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.1-Codex has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemma 4 31B or GPT-5.1-Codex?
GPT-5.1-Codex has the higher public point estimate, 50.88 versus 28.81. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemma 4 31B or GPT-5.1-Codex?
GPT-5.1-Codex scores higher for coding on the public lane, 36.3 to 28.8. GPT-5.1-Codex is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; 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-5.1-Codex?
GPT-5.1-Codex is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemma 4 31B or GPT-5.1-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-5.1-Codex?
GPT-5.1-Codex has the larger documented context window: 400K, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Gemma 4 31B
API / mo (Novita AI)$405
Self-host / mo$298
Break-even37M/day
GPT-5.1-Codex
API / mo$8,438
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 evidence16 rows
Agentic
b3 Agent Security
Gemma 4 31B83.0%SourceGPT-5.1-Codex—Not directly comparable
Cyber Misuse Refusal
Gemma 4 31B84.0%SourceGPT-5.1-Codex—Not directly comparable
Gert Labs
Shared sourceGemma 4 31B35.26%GPT-5.1-Codex49.68%GPT-5.1-Codex leads this result
JobBench
Gemma 4 31B—GPT-5.1-Codex26.2%SourceNot directly comparable
Coding
LiveCodeBench v6
Gemma 4 31B80.0%SourceGPT-5.1-Codex—Not directly comparable
SWE-Rebench
Gemma 4 31B41.6%SourceGPT-5.1-Codex—Not directly comparable
React Native Evals
Gemma 4 31B75.2%SourceGPT-5.1-Codex—Not directly comparable
Vibe Code Bench
Gemma 4 31B—GPT-5.1-Codex13.12%SourceNot directly comparable
LiveCodeBench (Vals)
Gemma 4 31B—GPT-5.1-Codex85.6%SourceNot directly comparable
Reasoning
MRCRv2
Gemma 4 31B66.4%SourceGPT-5.1-Codex—Not directly comparable
Multimodal
Knowledge
16 public results · 1 shared
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Last updated November 13, 2026