Long documents
Prompts that approach the documented context limit
Kimi K2.7 Code
Kimi K2.7 Code has the larger documented context window.
Confidence: documented
Gemma 4 E4B vs Kimi K2.7 Code
Updated November 13, 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.
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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.
- API price per 1M tokens
Gemma 4 E4B: No comparable API price
Kimi K2.7 Code: $0.95 input / $4 output
- Context window
Gemma 4 E4B: 128K
Kimi K2.7 Code: 256K
- Released
Gemma 4 E4B: April 2, 2026
Kimi K2.7 Code: June 12, 2026
- Shared results
- 0
- Gemma 4 E4B only
- 7
- Kimi K2.7 Code only
- 13
- Like-for-like categories
- 0 / 8
Estimated: Gemma 4 E4B and Kimi K2.7 Code. Conditional ranges do not establish rank confidence.How 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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 E4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 E4B is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate.
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.
14.7Gemma 4 E4B41.8Kimi K2.7 Code
Directional only · BenchAlign v5.8
Kimi K2.7 Code 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.
Same basis rules as the category table below
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Gemma 4 E4B only
13 Kimi K2.7 Code only
4 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 v5.8 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.
Coding
Directional only- Gemma 4 E4B
- 14.7
- Estimated · #151/162
- Kimi K2.7 Code
- 41.8
- Supported · #68/162
- Basis
- BenchAlign v5.8 lane · 1 vs 7 public rows
- Reading
- Directional only
Reasoning
Directional only- Gemma 4 E4B
- 21.8
- Supported · #140/160
- Kimi K2.7 Code
- 48.4
- Estimated · #56/160
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemma 4 E4B
- 27.5
- Estimated · #159/180
- Kimi K2.7 Code
- 55.3
- Estimated · #64/180
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemma 4 E4B
- 51.9
- #95/157
- Kimi K2.7 Code
- 76.6
- #68/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemma 4 E4B
- Not ranked
- Kimi K2.7 Code
- 38.7
- Supported · #74/135
- Basis
- BenchAlign v5.8 lane · 0 vs 4 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemma 4 E4B
- 28.6
- Supported · #67/67
- Kimi K2.7 Code
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemma 4 E4B
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemma 4 E4B
- Not ranked
- Kimi K2.7 Code
- 54.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemma 4 E4B | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Coding | 14.7Estimated · #151/162 | 41.8Supported · #68/162 | Directional onlyBenchAlign v5.8 lane · 1 vs 7 public rows | Directional only |
| Reasoning | 21.8Supported · #140/160 | 48.4Estimated · #56/160 | Directional onlyBenchAlign v5.8 lane · 1 vs 0 public rows | Directional only |
| Knowledge | 27.5Estimated · #159/180 | 55.3Estimated · #64/180 | Directional onlyBenchAlign v5.8 lane · 2 vs 0 public rows | Directional only |
| Instruction following | 51.9#95/157 | 76.6#68/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 38.7Supported · #74/135 | Not comparableBenchAlign v5.8 lane · 0 vs 4 public rows | Not comparable |
| Multimodal | 28.6Supported · #67/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 54.2Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 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.
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 E4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Kimi K2.7 Code
- $0.00295
- Fits in one request
Gemma 4 E4B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Kimi K2.7 Code
- $0.0595
- Fits in one request
Gemma 4 E4B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemma 4 E4B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Kimi K2.7 Code
- $0.097
- Fits in one request
Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Price against score, among all priced models
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
Kimi K2.7 Code
- Gemma 4 E4B has no public API price, so it is not plotted.
- 14 models score at least as high as Kimi K2.7 Code for the same price or less. The nearest is Kimi K2.6 (score 56.6, also $4.00 per 1M output tokens).
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 Kimi K2.7 Code on the full price vs performance chartSpecification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemma 4 E4BKimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
Gemma 4 E4B
Kimi K2.7 Code
256K
API model ID
Gemma 4 E4B
Not sourced
Kimi 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 E4B
No comparable hosted API rate
Kimi K2.7 Code
$0.19 per 1M cached input tokens
Documented inputs
Gemma 4 E4B
text, image, audio, video
Google Gemma 4 model documentationKimi K2.7 Code
text, image, video
Kimi-K2.7-Code model cardDocumented outputs
Gemma 4 E4B
Kimi K2.7 Code
Provider availability
Gemma 4 E4B
Open Weights · open weights
Google gemma-4-E4B model cardKimi K2.7 Code
Not sourced
Reasoning profile
Gemma 4 E4B
Reasoning
Kimi K2.7 Code
Reasoning
Weight access
Gemma 4 E4B
Open Weight
Kimi K2.7 Code
Open Weight
License
Gemma 4 E4B
Open Weight
Kimi K2.7 Code
Open Weight
Release date
Gemma 4 E4B
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
- Kimi K2.7 Code has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemma 4 E4B 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 E4B or Kimi K2.7 Code?
Kimi K2.7 Code scores higher for coding on the public lane, 41.8 to 14.7. Gemma 4 E4B is 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 E4B or Kimi K2.7 Code?
Gemma 4 E4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemma 4 E4B 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 E4B or Kimi K2.7 Code?
Kimi K2.7 Code has the larger documented context window: 256K, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Gemma 4 E4B
API / moNo API price on file
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$27,962
Break-evenNot reached at 70% load
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence20 rows
Agentic
Kimi Claw 24/7
Gemma 4 E4B—Kimi K2.7 Code46.9%SourceNot directly comparable
MCP Atlas
Gemma 4 E4B—Kimi K2.7 Code76%SourceNot directly comparable
MCP Mark Verified
Gemma 4 E4B—Kimi K2.7 Code81.1%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 E4B—Kimi K2.7 Code67.0%SourceNot directly comparable
Coding
LiveCodeBench v6
Gemma 4 E4B52.0%SourceKimi K2.7 Code—Not directly comparable
Kimi Code Bench v2
Gemma 4 E4B—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
Gemma 4 E4B—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
Gemma 4 E4B—Kimi K2.7 Code35.1%SourceNot directly comparable
CursorBench 3.2
Gemma 4 E4B—Kimi K2.7 Code49.7%SourceNot directly comparable
OpenHarmony Bench v1.0
Gemma 4 E4B—Kimi K2.7 Code52.1%SourceNot directly comparable
LiveCodeBench (Vals)
Gemma 4 E4B—Kimi K2.7 Code82.1%SourceNot directly comparable
SWE-bench (Vals)
Gemma 4 E4B—Kimi K2.7 Code78.2%SourceNot directly comparable
Reasoning
MRCRv2
Gemma 4 E4B25.4%SourceKimi K2.7 Code—Not directly comparable
Multimodal
Knowledge
Math
20 public results · 0 shared
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Last updated November 13, 2026