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.
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.
Gemma 4 26B A4B: No comparable API price
Kimi K2.7 Code: $0.95 input / $4 output
Gemma 4 26B A4B: 256K
Kimi K2.7 Code: 256K
Gemma 4 26B A4B: April 2, 2026
Kimi K2.7 Code: June 12, 2026
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.
Code generation, repair, and software-engineering tasks
Kimi K2.7 Code
Kimi K2.7 Code has the higher public coding point estimate, 41.8 to 30.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 26B A4B is not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
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.
| Category | Gemma 4 26B A4B | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Coding | 30.6Supported · #103/162 | 41.8Supported · #68/162 | Like-for-likeBenchAlign v5.8 lane · 2 vs 7 public rows | Kimi K2.7 Code leads · intervals overlap |
| Reasoning | 35.0Supported · #102/160 | 48.4Estimated · #56/160 | Directional onlyBenchAlign v5.8 lane · 1 vs 0 public rows | Directional only |
| Knowledge | 41.4Estimated · #116/180 | 55.3Estimated · #64/180 | Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows | Directional only |
| Instruction following | 88.7#37/157 | 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 | 48.6Supported · #56/67 | 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.
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 26B A4B has no comparable published API token rate.
50K fresh input + 3K output tokens
Gemma 4 26B A4B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Gemma 4 26B A4B has no comparable published API token rate.
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
Kimi K2.7 Code
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 chartSourced 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 26B A4B
Kimi K2.7 Code
256K
Gemma 4 26B A4B
gemma-4-26b-a4b-it
Google Gemma Gemini API guideKimi K2.7 Code
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 26B A4B
No comparable hosted API rate
Kimi K2.7 Code
$0.19 per 1M cached input tokens
Gemma 4 26B A4B
text, image
Google Gemma 4 model documentationKimi K2.7 Code
text, image, video
Kimi-K2.7-Code model cardGemma 4 26B A4B
Kimi K2.7 Code
Gemma 4 26B A4B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideKimi K2.7 Code
Not sourced
Gemma 4 26B A4B
Reasoning
Kimi K2.7 Code
Reasoning
Gemma 4 26B A4B
Open Weight
Kimi K2.7 Code
Open Weight
Gemma 4 26B A4B
Open Weight
Kimi K2.7 Code
Open Weight
Gemma 4 26B A4B
2026-04-02
Kimi K2.7 Code
2026-06-12
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.
Kimi K2.7 Code has the higher public coding point estimate, 41.8 to 30.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 256K.
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.
Kimi Claw 24/7
Not directly comparable
MCP Atlas
Not directly comparable
MCP Mark Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ScarfBench
Not directly comparable
LiveCodeBench v6
Not directly comparable
Kimi Code Bench v2
Not directly comparable
ProgramBench
Not directly comparable
MLS-Bench Lite
Not directly comparable
CursorBench 3.2
Not directly comparable
OpenHarmony Bench v1.0
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MRCRv2
Not directly comparable
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Last updated November 13, 2026
