Agentic
Directional only- Gemma 4 12B
- 47.9
- Estimated · #81/151
- Kimi K2.5 (Reasoning)
- 49.9
- Estimated · #67/151
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
Gemma 4 12B vs Kimi K2.5 (Reasoning)
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K2.5 (Reasoning) has the higher public score estimate, 58.74 versus 46.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 12B and Kimi K2.5 (Reasoning) are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 12B and Kimi K2.5 (Reasoning) are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
9 Gemma 4 12B only
3 Shared
6 Kimi K2.5 (Reasoning) only
- Shared results
- 3
- Gemma 4 12B only
- 9
- Kimi K2.5 (Reasoning) only
- 6
- Like-for-like categories
- 0 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, 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 12B
- 46.6
- Estimated · #97/183
- Kimi K2.5 (Reasoning)
- 52.9
- Estimated · #52/183
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemma 4 12B
- 40.4
- Supported · #138/181
- Kimi K2.5 (Reasoning)
- 52.4
- Estimated · #78/181
- Basis
- BenchAlign lane · 5 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemma 4 12B
- 34.0
- Unranked · 4 rankable rows
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 12B
- 57.6
- Unranked · 1 rankable row
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 12B
- Not ranked
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 12B
- 26.1
- #44/48
- Kimi K2.5 (Reasoning)
- 63.2
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemma 4 12B
- 89.8
- #21/120
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 12B | Kimi K2.5 (Reasoning) | Basis | Reading |
|---|---|---|---|---|
| Agentic | 47.9Estimated · #81/151 | 49.9Estimated · #67/151 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Coding | 46.6Estimated · #97/183 | 52.9Estimated · #52/183 | Directional onlyBenchAlign lane · 1 vs 2 public rows | Directional only |
| Knowledge | 40.4Supported · #138/181 | 52.4Estimated · #78/181 | Directional onlyBenchAlign lane · 5 vs 2 public rows | Directional only |
| Reasoning | 34.0Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 57.6Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 26.1#44/48 | 63.2Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Instruction following | 89.8#21/120 | 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, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
MMLU-Pro
Knowledge
Gemma 4 12B: 77.2%Kimi K2.5 (Reasoning): 87.1%Normalized gap 9.9Gemma 4 12B source Kimi K2.5 (Reasoning) sourceMMMU-Pro
Multimodal
Gemma 4 12B: 69.1%Kimi K2.5 (Reasoning): 78.5%Normalized gap 9.4Gemma 4 12B source Kimi K2.5 (Reasoning) sourceGPQA
Knowledge
Gemma 4 12B: 78.8%Kimi K2.5 (Reasoning): 87.6%Normalized gap 8.8Gemma 4 12B source Kimi K2.5 (Reasoning) source
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 12B
- API rate not published
- Fits in one request
- Kimi K2.5 (Reasoning)
- $0.0021
- Fits in one request
Gemma 4 12B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 12B
- API rate not published
- Fits in one request
- Kimi K2.5 (Reasoning)
- $0.039
- Fits in one request
Gemma 4 12B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 12B
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Kimi K2.5 (Reasoning)
- $0.162
- Fits in one request
- Cached input priced at the published list-input rate
Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 12B 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 12BKimi K2.5 (Reasoning)
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 12B
Kimi K2.5 (Reasoning)
256K
API model ID
Gemma 4 12B
Not sourced
Kimi K2.5 (Reasoning)
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 12B
No comparable hosted API rate
Kimi K2.5 (Reasoning)
Not published
Documented inputs
Gemma 4 12B
text, image, audio, video
Google Gemma 4 model documentationKimi K2.5 (Reasoning)
Not sourced
Documented outputs
Gemma 4 12B
Kimi K2.5 (Reasoning)
Not sourced
Provider availability
Gemma 4 12B
Open Weights · open weights
Google gemma-4-12B model cardKimi K2.5 (Reasoning)
Not sourced
Reasoning profile
Gemma 4 12B
Reasoning
Kimi K2.5 (Reasoning)
Reasoning
Weight access
Gemma 4 12B
Open Weight
Kimi K2.5 (Reasoning)
Proprietary
License
Gemma 4 12B
Open Weight
Kimi K2.5 (Reasoning)
Proprietary
Release date
Gemma 4 12B
2026-06-03
Kimi K2.5 (Reasoning)
2026-02-01
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
- Kimi K2.5 (Reasoning) has the higher public score estimate, 58.74 versus 46.64, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 256K.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence18 rows
Agentic
Coding
Reasoning
Knowledge
GPQA
Gemma 4 12B78.8%SourceKimi K2.5 (Reasoning)87.6%SourceKimi K2.5 (Reasoning) leads this result
GPQA-D
Gemma 4 12B78.8%SourceKimi K2.5 (Reasoning)—Not directly comparable
MMLU-Pro
Gemma 4 12B77.2%SourceKimi K2.5 (Reasoning)87.1%SourceKimi K2.5 (Reasoning) leads this result
HLE w/o tools
Gemma 4 12B5.2%SourceKimi K2.5 (Reasoning)—Not directly comparable
MMMLU
Gemma 4 12B83.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Gemma 4 12B or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the higher public score estimate, 58.74 versus 46.64, 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 12B or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) scores higher for coding on the public lane, 52.9 to 46.6. Gemma 4 12B and Kimi K2.5 (Reasoning) are 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 12B or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) scores higher for agentic tasks on the public lane, 49.9 to 47.9. Gemma 4 12B and Kimi K2.5 (Reasoning) are 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.
Which costs less, Gemma 4 12B or Kimi K2.5 (Reasoning)?
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 12B or Kimi K2.5 (Reasoning)?
Both models list the same context window, 256K.
Related comparisons
Last updated September 4, 2026
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