Grok 4.7 vs Kimi K3

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Grok 4.7

xAI

—

Evidence status unavailable

90% interval unavailable

Model B

Kimi K3

Moonshot AI

74.3/100

Supported · Public rank #8

90% interval 71.3–77.3

Updated September 21, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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.

Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Grok 4.7
Grok 4.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Grok 4.7
Grok 4.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
Grok 4.7
Grok 4.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Grok 4.7 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Grok 4.7 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited

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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—Grok 4.767.9Kimi K3

Not comparable · BenchAlign

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

4 Grok 4.7 only

2 Shared

42 Kimi K3 only

Shared results: 2
Grok 4.7 only: 4
Kimi K3 only: 42
Like-for-like categories: 0 / 8

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.

Agentic

Not comparable

Grok 4.7: Not ranked
Kimi K3: 72.1; Supported · #4/154
Basis: BenchAlign lane · 2 vs 12 public rows
Reading: Not comparable

Coding

Not comparable

Grok 4.7: Not ranked
Kimi K3: 67.9; Supported · #9/156
Basis: BenchAlign lane · 3 vs 13 public rows
Reading: Not comparable

Reasoning

Not comparable

Grok 4.7: 73.7; Unranked · 2 rankable rows
Kimi K3: 78.5; #3/19
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Grok 4.7: Not ranked
Kimi K3: 89.4; #1/49
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Grok 4.7: Not ranked
Kimi K3: 72.1; Supported · #8/186
Basis: BenchAlign lane · 1 vs 6 public rows
Reading: Not comparable

Multilingual

Not comparable

Grok 4.7: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Grok 4.7: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Grok 4.7: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Grok 4.7 and Kimi K3
Category	Grok 4.7	Kimi K3	Basis	Reading
Agentic	Not ranked	72.1Supported · #4/154	Not comparableBenchAlign lane · 2 vs 12 public rows	Not comparable
Coding	Not ranked	67.9Supported · #9/156	Not comparableBenchAlign lane · 3 vs 13 public rows	Not comparable
Reasoning	73.7Unranked · 2 rankable rows	78.5#3/19	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	89.4#1/49	Not comparableProvisional lane · 0 vs 3 weighted rows	Not comparable
Knowledge	Not ranked	72.1Supported · #8/186	Not comparableBenchAlign lane · 1 vs 6 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	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) 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Grok 4.7: $0.005; Fits in one request
Kimi K3: $0.0105; Fits in one request

Grok 4.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Grok 4.7: $0.118; Fits in one request
Kimi K3: $0.195; Fits in one request

Grok 4.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Grok 4.7: $0.2; Fits in one request
Kimi K3: $0.27; Fits in one request

Grok 4.7 has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGrok 4.7Kimi K3

Context window

Maximum documented context; output-token limits may be lower.

Grok 4.7

500K

xAI Grok 4.7 model documentation

Kimi K3

1.05M

API model ID

Grok 4.7

grok-4.7

xAI Grok 4.7 model documentation

Kimi K3

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Grok 4.7

$0.5 per 1M cached input tokens

xAI Grok 4.7 model documentation

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Grok 4.7

text, image

xAI Grok 4.7 model documentation

Kimi K3

Not sourced

Documented outputs

Grok 4.7

text

xAI Grok 4.7 model documentation

Kimi K3

Not sourced

Provider availability

Grok 4.7

Generally Available · xAI API, xAI US regional endpoint, Grok Build, Cursor, model gateways

xAI Grok 4.7 model documentation

Kimi K3

Not sourced

Reasoning profile

Grok 4.7

Reasoning

Kimi K3

Reasoning

Weight access

Grok 4.7

Proprietary

Kimi K3

Pending

License

Grok 4.7

Proprietary

Kimi K3

Pending

Release date

Grok 4.7

2026-09-21

Kimi K3

2026-07-16

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: Repository review: $0.118 vs $0.195. Cache-heavy agent loop: $0.2 vs $0.27.
Context tradeoff: Kimi K3 has the larger documented window (1.05M).

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 evidence48 rows

Agentic

Terminal-Bench 4.0
Grok 4.738.00%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Grok 4.776.0%
Source
Kimi K380.9%
Source
Kimi K3 leads this result
Terminal-Bench 2.0
Grok 4.7—
Kimi K388.3%
Source
Not directly comparable
BrowseComp
Grok 4.7—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
Grok 4.7—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
Grok 4.7—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
Grok 4.7—
Kimi K384.2%
Source
Not directly comparable
AutomationBench
Grok 4.7—
Kimi K330.8%
Source
Not directly comparable
JobBench
Grok 4.7—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
Grok 4.7—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
Grok 4.7—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
Grok 4.7—
Kimi K373.5%
Source
Not directly comparable
ApprenticeBench
Grok 4.7—
Kimi K318%
Source
Not directly comparable

Coding

cursorBench40
Grok 4.746.3%
Source
Kimi K3—
Not directly comparable
DeepSWE
Grok 4.771.0%
Source
Kimi K367.5%
Source
Grok 4.7 leads this result
EEBench
Grok 4.764.0%
Source
Kimi K3—
Not directly comparable
cursorBench32
Grok 4.7—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
Grok 4.7—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
Grok 4.7—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
Grok 4.7—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
Grok 4.7—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
Grok 4.7—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
Grok 4.7—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
Grok 4.7—
Kimi K373.7%
Source
Not directly comparable
OpenHarmony Bench
Grok 4.7—
Kimi K357.3%
Source
Not directly comparable
FrontierSWE v2
Grok 4.7—
Kimi K325.9%
Source
Not directly comparable
LiveCodeBench (Vals)
Grok 4.7—
Kimi K387.2%
Source
Not directly comparable
SWE-bench (Vals)
Grok 4.7—
Kimi K393.4%
Source
Not directly comparable

Multimodal

OfficeQA Pro
Grok 4.7—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
Grok 4.7—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
Grok 4.7—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
Grok 4.7—
Kimi K384.8%
Source
Not directly comparable
CharXiv
Grok 4.7—
Kimi K391.3%
Source
Not directly comparable
MathVision
Grok 4.7—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
Grok 4.7—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
Grok 4.7—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
Grok 4.7—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
Grok 4.7—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
Grok 4.7—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
Grok 4.7—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
Grok 4.7—
Kimi K358.5%
Source
Not directly comparable

Knowledge

HealthBench Professional
Grok 4.756.7%
Source
Kimi K3—
Not directly comparable
GPQA
Grok 4.7—
Kimi K393.5%
Source
Not directly comparable
GPQA-D
Grok 4.7—
Kimi K393.5%
Source
Not directly comparable
HLE
Grok 4.7—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
Grok 4.7—
Kimi K343.5%
Source
Not directly comparable
GPQA Diamond (Vals)
Grok 4.7—
Kimi K392.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Grok 4.7—
Kimi K388.0%
Source
Not directly comparable

Questions

Which is better, Grok 4.7 or Kimi K3?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Grok 4.7 or Kimi K3?

Grok 4.7 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Grok 4.7 or Kimi K3?

Grok 4.7 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Grok 4.7 or Kimi K3?

For the stated presets, chat costs $0.005 on Grok 4.7 and $0.0105 on Kimi K3; repository review costs $0.118 and $0.195; the cache-heavy agent loop costs $0.2 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, Grok 4.7 or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 500K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 21, 2026

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Grok 4.7 vs Kimi K3

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

Which one for a specific job

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Questions

Which is better, Grok 4.7 or Kimi K3?

Which is better for coding, Grok 4.7 or Kimi K3?

Which is better for agentic tasks, Grok 4.7 or Kimi K3?

Which costs less, Grok 4.7 or Kimi K3?

Which has the larger context window, Grok 4.7 or Kimi K3?

Related comparisons

Watch Grok 4.7 vs Kimi K3