Coding
Directional only- Gemma 4 31B
- 41.6
- MiniMax M2.7
- 53.3
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Gemma 4 31B vs MiniMax M2.7
Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
MiniMax M2.7 has the higher public score estimate, 63.06 versus 60.15, 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.
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
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 31B has no comparable published API token rate.
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.
5 Gemma 4 31B only
3 Shared
15 MiniMax M2.7 only
- Shared results
- 3
- Gemma 4 31B only
- 5
- MiniMax M2.7 only
- 15
- 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
- MiniMax M2.7
- 57.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemma 4 31B
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemma 4 31B
- 52.9
- MiniMax M2.7
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 31B
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 31B
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 31B
- 76.9
- MiniMax M2.7
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemma 4 31B
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemma 4 31B | MiniMax M2.7 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 41.6 | 53.3 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 57.0 | Not comparable0 vs 1 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
- MiniMax M2.7
- $0.0009
- 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
- MiniMax M2.7
- $0.0186
- 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
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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 31BMiniMax M2.7
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 31B
MiniMax M2.7
200K
API model ID
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideMiniMax M2.7
Not sourced
Cached-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
MiniMax M2.7
Not published
Documented inputs
Gemma 4 31B
text, image
Google Gemma 4 model documentationMiniMax M2.7
Not sourced
Documented outputs
Gemma 4 31B
MiniMax M2.7
Not sourced
Provider availability
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideMiniMax M2.7
Not sourced
Reasoning profile
Gemma 4 31B
Reasoning
MiniMax M2.7
Non-Reasoning
Weight access
Gemma 4 31B
Open Weight
MiniMax M2.7
Open Weight
License
Gemma 4 31B
Open Weight
MiniMax M2.7
Open Weight
Release date
Gemma 4 31B
2026-04-02
MiniMax M2.7
2026-03-18
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
- MiniMax M2.7 has the higher public score estimate, 63.06 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
- 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.
Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
MiniMax M2.7
API / mo$1,125
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 evidence23 rows
Agentic
Gert Labs
Shared sourceGemma 4 31B35.26%MiniMax M2.740.40%MiniMax M2.7 leads this result
Terminal-Bench 2.0
Gemma 4 31B—MiniMax M2.757%SourceNot directly comparable
Toolathlon
Gemma 4 31B—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
Gemma 4 31B—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
Gemma 4 31B—MiniMax M2.762.7%SourceNot directly comparable
Claw-Eval
Gemma 4 31B—MiniMax M2.748.7%SourceNot directly comparable
Coding
SWE-Rebench
Shared sourceGemma 4 31B41.6%MiniMax M2.751.9%MiniMax M2.7 leads this result
React Native Evals
Shared sourceGemma 4 31B75.2%MiniMax M2.771.4%Gemma 4 31B leads this result
SWE-bench Verified*
Gemma 4 31B—MiniMax M2.775.4%SourceNot directly comparable
SWE-bench Pro
Gemma 4 31B—MiniMax M2.756.2%SourceNot directly comparable
SWE Multilingual
Gemma 4 31B—MiniMax M2.776.5%SourceNot directly comparable
Multi-SWE Bench
Gemma 4 31B—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
Gemma 4 31B—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
Gemma 4 31B—MiniMax M2.739.8%SourceNot directly comparable
Vibe Code Bench
Gemma 4 31B—MiniMax M2.727.04%SourceNot directly comparable
Knowledge
GPQA
Gemma 4 31B84.3%SourceMiniMax M2.7—Not directly comparable
MMLU-Pro
Gemma 4 31B85.2%SourceMiniMax M2.7—Not directly comparable
HLE
Gemma 4 31B26.5%SourceMiniMax M2.7—Not directly comparable
HLE w/o tools
Gemma 4 31B19.5%SourceMiniMax M2.7—Not directly comparable
GPQA-D
Gemma 4 31B—MiniMax M2.787.0%SourceNot directly comparable
MMLU-Pro (Arcee)
Gemma 4 31B—MiniMax M2.780.8%SourceNot directly comparable
Math
AIME25 (Arcee)
Gemma 4 31B—MiniMax M2.780.0%SourceNot directly comparable
Multimodal
MMMU-Pro
Gemma 4 31B76.9%SourceMiniMax M2.7—Not directly comparable
Frequently asked questions
Which is better, Gemma 4 31B or MiniMax M2.7?
MiniMax M2.7 has the higher public score estimate, 63.06 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 MiniMax M2.7?
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 MiniMax M2.7?
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 MiniMax M2.7?
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 MiniMax M2.7?
Gemma 4 31B has the larger documented context window: 256K, compared with 200K.
Related comparisons
Last updated August 18, 2026
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