Updated November 13, 2026. Rank says Gemini 3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Gemini 3.1 Pro has the higher public point estimate, 63.11 versus 28.81. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Gemini 3.1 Pro: $2 input / $12 output
Gemma 4 31B: No comparable API price
Gemini 3.1 Pro: 1M
Gemma 4 31B: 256K
Gemini 3.1 Pro: February 19, 2026
Gemma 4 31B: April 2, 2026
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.
Code generation, repair, and software-engineering tasks
Gemini 3.1 Pro
Gemini 3.1 Pro has the higher public coding point estimate, 37.9 to 28.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Gemini 3.1 Pro
Gemini 3.1 Pro has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 31B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
Gemini 3.1 Pro 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
HLE w/o toolsKnowledge
Normalized gap 25.9MMMU-ProMultimodal
Normalized gap 7.0Each 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.
| Category | Gemini 3.1 Pro | Gemma 4 31B | Basis | Reading |
|---|---|---|---|---|
| Coding | 37.9Supported · #76/162 | 28.8Supported · #107/162 | Like-for-likeBenchAlign v5.8 lane · 9 vs 3 public rows | Gemini 3.1 Pro leads · intervals overlap |
| Knowledge | 66.0Supported · #24/180 | 36.7Supported · #121/180 | Like-for-likeBenchAlign v5.8 lane · 6 vs 4 public rows | Gemini 3.1 Pro leads |
| Agentic | 39.6Supported · #72/135 | 12.5Estimated · #133/135 | Directional onlyBenchAlign v5.8 lane · 7 vs 3 public rows | Directional only |
| Reasoning | 56.3Estimated · #44/160 | 32.0Supported · #109/160 | Directional onlyBenchAlign v5.8 lane · 2 vs 1 public rows | Directional only |
| Multimodal | 65.3Estimated · #27/67 | 47.3Supported · #60/67 | Directional onlyBenchAlign v5.8 lane · 8 vs 2 public rows | Directional only |
| Instruction following | 90.2#27/157 | 92.9#16/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 60.6Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 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.
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.
1K fresh input + 500 output tokens
Gemma 4 31B has no comparable published API token rate.
50K fresh input + 3K output tokens
Gemma 4 31B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Gemma 4 31B has no comparable published API token rate.
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
Gemini 3.1 Pro
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 Gemini 3.1 Pro on the full price vs performance chartSourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Gemini 3.1 Pro
Gemma 4 31B
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationGemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingGemma 4 31B
No comparable hosted API rate
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationGemma 4 31B
text, image
Google Gemma 4 model documentationGemini 3.1 Pro
Gemma 4 31B
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini 3.1 Pro Preview model documentationGemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideGemini 3.1 Pro
Reasoning
Gemma 4 31B
Reasoning
Gemini 3.1 Pro
Proprietary
Gemma 4 31B
Open Weight
Gemini 3.1 Pro
Proprietary
Gemma 4 31B
Open Weight
Gemini 3.1 Pro
2026-02-19
Gemma 4 31B
2026-04-02
Gemini 3.1 Pro has the higher public point estimate, 63.11 versus 28.81. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Gemini 3.1 Pro has the higher public coding point estimate, 37.9 to 28.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemini 3.1 Pro scores higher for agentic tasks on the public lane, 39.6 to 12.5. Gemma 4 31B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Gemini 3.1 Pro has the larger documented context window: 1M, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
τ²-bench results
Not directly comparable
Gert Labs
Shared sourceGemini 3.1 Pro leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
MazeBench
Not directly comparable
b3 Agent Security
Not directly comparable
Cyber Misuse Refusal
Not directly comparable
ScarfBench
Not directly comparable
LiveCodeBench Pro
Not directly comparable
React Native Evals
Shared sourceGemini 3.1 Pro leads this result
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
PostTrainBench v1.1
Not directly comparable
PostTrainBench
Not directly comparable
PostTrainBench v1
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-Rebench
Not directly comparable
ParseBench-1
Shared sourceGemini 3.1 Pro leads this result
MMMU-Pro
Gemini 3.1 Pro leads this result
CharXiv
Not directly comparable
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
ZeroBench
Not directly comparable
MedXpertQA (MM)
Not directly comparable
GPQA-D
Not directly comparable
HLE w/o tools
Gemini 3.1 Pro leads this result
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA
Not directly comparable
MMLU-Pro
Not directly comparable
HLE
Not directly comparable
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Last updated November 13, 2026
