Coding work
Code generation, repair, and software-engineering tasks
Kimi K2.7 Code
Kimi K2.7 Code leads on the public coding lane, 44.5 to 35.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Gemma 4 31B vs Kimi K2.7 Code
Updated September 29, 2026. Rank says Kimi K2.7 Code is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Gemma 4 31B only
- 8
- Kimi K2.7 Code only
- 11
- Like-for-like categories
- 1 / 8
Estimated: Gemma 4 31B and Kimi K2.7 CodeHow 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
Agentic work
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.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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.
35.5Gemma 4 31B44.5Kimi K2.7 Code
Like-for-like · BenchAlign v5.7
Kimi K2.7 Code leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Gemma 4 31B only
11 Kimi K2.7 Code only
3 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 v5.7 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
Like-for-like- Gemma 4 31B
- 35.5
- Supported · #80/143
- Kimi K2.7 Code
- 44.5
- Supported · #54/143
- Basis
- BenchAlign v5.7 lane · 2 vs 7 public rows
- Reading
- Kimi K2.7 Code leads · intervals overlap
Agentic
Directional only- Gemma 4 31B
- 24.4
- Estimated · #92/117
- Kimi K2.7 Code
- 37.2
- Supported · #59/117
- Basis
- BenchAlign v5.7 lane · 1 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemma 4 31B
- 40.0
- Supported · #99/169
- Kimi K2.7 Code
- 53.0
- Estimated · #53/169
- Basis
- BenchAlign v5.7 lane · 4 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 31B
- 91.5
- #12/124
- Kimi K2.7 Code
- 75.1
- #59/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Gemma 4 31B
- 70.2
- Unranked · 2 rankable rows
- Kimi K2.7 Code
- 76.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 31B
- 59.5
- #32/50
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 31B
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 31B
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 31B | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Coding | 35.5Supported · #80/143 | 44.5Supported · #54/143 | Like-for-likeBenchAlign v5.7 lane · 2 vs 7 public rows | Kimi K2.7 Code leads · intervals overlap |
| Agentic | 24.4Estimated · #92/117 | 37.2Supported · #59/117 | Directional onlyBenchAlign v5.7 lane · 1 vs 4 public rows | Directional only |
| Knowledge | 40.0Supported · #99/169 | 53.0Estimated · #53/169 | Directional onlyBenchAlign v5.7 lane · 4 vs 0 public rows | Directional only |
| Instruction following | 91.5#12/124 | 75.1#59/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 70.2Unranked · 2 rankable rows | 76.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 59.5#32/50 | 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 |
| 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, and knowledge denominators (BenchAlign v5.7) 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
- Kimi K2.7 Code
- $0.00295
- 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
- Kimi K2.7 Code
- $0.0595
- 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
- Kimi K2.7 Code
- $0.097
- 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
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemma 4 31BKimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 31B
Kimi K2.7 Code
256K
API model ID
Gemma 4 31B
gemma-4-31b-it
Google Gemma Gemini API guideKimi K2.7 Code
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
Kimi K2.7 Code
$0.19 per 1M cached input tokens
Documented inputs
Gemma 4 31B
text, image
Google Gemma 4 model documentationKimi K2.7 Code
Not sourced
Documented outputs
Gemma 4 31B
Kimi K2.7 Code
Not sourced
Provider availability
Gemma 4 31B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideKimi K2.7 Code
Not sourced
Reasoning profile
Gemma 4 31B
Reasoning
Kimi K2.7 Code
Reasoning
Weight access
Gemma 4 31B
Open Weight
Kimi K2.7 Code
Open Weight
License
Gemma 4 31B
Open Weight
Kimi K2.7 Code
Open Weight
Release date
Gemma 4 31B
2026-04-02
Kimi K2.7 Code
2026-06-12
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- 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.
Questions
Which is better, Gemma 4 31B or Kimi K2.7 Code?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemma 4 31B or Kimi K2.7 Code?
Kimi K2.7 Code leads the public coding lane, 44.5 to 35.5, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemma 4 31B or Kimi K2.7 Code?
Kimi K2.7 Code scores higher for agentic tasks on the public lane, 37.2 to 24.4. 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.
Which costs less, Gemma 4 31B or Kimi K2.7 Code?
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 Kimi K2.7 Code?
Both models list the same context window, 256K.
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—
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence19 rows
Agentic
Gert Labs
Gemma 4 31B35.26%SourceKimi K2.7 Code—Not directly comparable
Kimi Claw 24/7
Gemma 4 31B—Kimi K2.7 Code46.9%SourceNot directly comparable
MCP Atlas
Gemma 4 31B—Kimi K2.7 Code76%SourceNot directly comparable
MCP Mark Verified
Gemma 4 31B—Kimi K2.7 Code81.1%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 31B—Kimi K2.7 Code67.0%SourceNot directly comparable
Coding
SWE-Rebench
Gemma 4 31B41.6%SourceKimi K2.7 Code—Not directly comparable
React Native Evals
Gemma 4 31B75.2%SourceKimi K2.7 Code—Not directly comparable
Kimi Code Bench v2
Gemma 4 31B—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
Gemma 4 31B—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
Gemma 4 31B—Kimi K2.7 Code35.1%SourceNot directly comparable
cursorBench32
Gemma 4 31B—Kimi K2.7 Code49.7%SourceNot directly comparable
OpenHarmony Bench
Gemma 4 31B—Kimi K2.7 Code52.1%SourceNot directly comparable
LiveCodeBench (Vals)
Gemma 4 31B—Kimi K2.7 Code82.1%SourceNot directly comparable
SWE-bench (Vals)
Gemma 4 31B—Kimi K2.7 Code78.2%SourceNot directly comparable
Multimodal
MMMU-Pro
Gemma 4 31B76.9%SourceKimi K2.7 Code—Not directly comparable
Knowledge
19 public results · 0 shared
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Last updated September 29, 2026