Knowledge
Directional only- Gemma 4 31B
- 52.9
- o1
- 75.7
- Weighted basis
- 3 vs 1 rows
- Reading
- Directional only
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start free briefUpdated 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 48.17, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Prompts that approach the documented context limit
Gemma 4 31B
Gemma 4 31B has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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
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. o1 does not fit this workload in one request. o1 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
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | Gemma 4 31B | o1 | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 52.9 | 75.7 | Directional only3 vs 1 rows | Directional only |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | 41.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | 9.3 | Not comparable0 vs 1 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 | 92.2 | Not comparable0 vs 1 rows | Not comparable |
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.
GPQA
Knowledge
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
o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 31B has no comparable published API token rate.
Sourced 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.
Gemma 4 31B
o1
200K
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideo1
Not sourced
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
o1
Not published
Gemma 4 31B
text, image
Google Gemma 4 model documentationo1
Not sourced
Gemma 4 31B
o1
Not sourced
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideo1
Not sourced
Gemma 4 31B
Reasoning
o1
Reasoning
Gemma 4 31B
Open Weight
o1
Proprietary
Gemma 4 31B
Open Weight
o1
Proprietary
Gemma 4 31B
2026-04-02
o1
2024-12-01
Run the same representative tasks against both endpoints before changing production traffic.
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.
Gert Labs
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
MMMU-Pro
Not directly comparable
IFEval
Not directly comparable
Gemma 4 31B has the higher public score estimate, 60.15 versus 48.17, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Gemma 4 31B has the larger documented context window: 256K, compared with 200K.
Last updated August 18, 2026
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