Model A

Moonshot AI

80.5/100

Supported · Public rank #5

90% interval 77.7–83.4

Kimi K3 vs Step 3.7 Flash

Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Step 3.7 Flash

StepFun

50.9/100

Estimated · Public rank #121

90% interval 39.4–62.4

Decision reading

Kimi K3 has the higher public score, 80.53 versus 50.89, and the 90% score intervals do not overlap.

3 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

Agentic work
Tool use, computer use, and multi-step task completion
Kimi K3
Kimi K3 leads on the same 2 weighted benchmark rows.
Confidence: limited
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
Step 3.7 Flash
Step 3.7 Flash 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

Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Step 3.7 Flash
Step 3.7 Flash has the lower estimated token cost for this stated workload. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Step 3.7 Flash
Step 3.7 Flash 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
No shared weighted benchmark basis supports a winner.
Confidence: limited

What is actually comparable

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

37 Kimi K3 only

3 Shared

8 Step 3.7 Flash only

Shared results: 3
Kimi K3 only: 37
Step 3.7 Flash only: 8
Like-for-like categories: 1 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Like-for-like

Kimi K3: 89.5
Step 3.7 Flash: 66.4
Weighted basis: 2 vs 2 rows
Reading: Kimi K3 leads

Coding

Not comparable

Kimi K3: Not measured
Step 3.7 Flash: 56.3
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Kimi K3: Not measured
Step 3.7 Flash: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

Kimi K3: 61.0
Step 3.7 Flash: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Math

Not comparable

Kimi K3: Not measured
Step 3.7 Flash: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Kimi K3: Not measured
Step 3.7 Flash: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Kimi K3: 78.5
Step 3.7 Flash: Not measured
Weighted basis: 3 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Kimi K3: Not measured
Step 3.7 Flash: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Kimi K3 and Step 3.7 Flash
Category	Kimi K3	Step 3.7 Flash	Weighted basis	Reading
Agentic	89.5	66.4	Like-for-like2 vs 2 rows	Kimi K3 leads
Coding	Not measured	56.3	Not comparable0 vs 1 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Knowledge	61.0	Not measured	Not comparable2 vs 0 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	78.5	Not measured	Not comparable3 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

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.

Terminal-Bench 2.0
Agentic
Kimi K3: 88.3%Step 3.7 Flash: 59.5%Normalized gap 28.8Kimi K3 source Step 3.7 Flash source
BrowseComp
Agentic
Kimi K3: 91.2%Step 3.7 Flash: 75.8%Normalized gap 15.4Kimi K3 source Step 3.7 Flash 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

Kimi K3: $0.0105; Fits in one request
Step 3.7 Flash: $0.00077; Fits in one request

Step 3.7 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Kimi K3: $0.195; Fits in one request
Step 3.7 Flash: $0.01345; Fits in one request

Step 3.7 Flash has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Kimi K3: $0.27; Fits in one request
Step 3.7 Flash: $0.0555; Fits in one request; Cached input priced at the published list-input rate

Step 3.7 Flash has the lower modeled cost

Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

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

SpecificationKimi K3Step 3.7 Flash

Context window

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

Kimi K3

1.05M

Step 3.7 Flash

256K

API model ID

Kimi K3

Not sourced

Step 3.7 Flash

Not sourced

Cached-input rate

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

Kimi K3

$0.3 per 1M cached input tokens

Step 3.7 Flash

Not published

Documented inputs

Kimi K3

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

Kimi K3

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

Kimi K3

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

Kimi K3

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Kimi K3

Pending

Step 3.7 Flash

Open Weight

License

Kimi K3

Pending

Step 3.7 Flash

Open Weight

Release date

Kimi K3

2026-07-16

Step 3.7 Flash

2026-05-29

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 K3 has the higher public score, 80.53 versus 50.89, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.195 vs $0.01345. Cache-heavy agent loop: $0.27 vs $0.0555.
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 2.0
Kimi K388.3%
Source
Step 3.7 Flash59.5%
Source
Kimi K3 leads this result
BrowseComp
Kimi K391.2%
Source
Step 3.7 Flash75.8%
Source
Kimi K3 leads this result
DeepSearchQA
Kimi K395.0%
Source
Step 3.7 Flash92.8%
Source
Kimi K3 leads this result
Toolathlon-Verified
Kimi K373.2%
Source
Step 3.7 Flash—
Not directly comparable
MCP Atlas
Kimi K384.2%
Source
Step 3.7 Flash—
Not directly comparable
AutomationBench
Kimi K330.8%
Source
Step 3.7 Flash—
Not directly comparable
JobBench
Kimi K352.9%
Source
Step 3.7 Flash—
Not directly comparable
APEX-Agents
Kimi K337.6%
Source
Step 3.7 Flash—
Not directly comparable
SpreadsheetBench 2
Kimi K334.8%
Source
Step 3.7 Flash—
Not directly comparable
DECK-Bench
Kimi K373.5%
Source
Step 3.7 Flash—
Not directly comparable
Toolathlon
Kimi K3—
Step 3.7 Flash49.5%
Source
Not directly comparable
Claw-Eval
Kimi K3—
Step 3.7 Flash67.1%
Source
Not directly comparable
HLE w/ tools
Kimi K3—
Step 3.7 Flash47.2%
Source
Not directly comparable
Gert Labs
Kimi K3—
Step 3.7 Flash51.57%
Source
Not directly comparable

Coding

deepSwe
Kimi K367.5%
Source
Step 3.7 Flash—
Not directly comparable
cursorBench32
Kimi K360.8%
Source
Step 3.7 Flash—
Not directly comparable
FrontierSWE
Kimi K381.2%
Source
Step 3.7 Flash—
Not directly comparable
ProgramBench
Kimi K377.8%
Source
Step 3.7 Flash—
Not directly comparable
Kimi Code Bench v2
Kimi K372.9%
Source
Step 3.7 Flash—
Not directly comparable
sweMarathon
Kimi K342%
Source
Step 3.7 Flash—
Not directly comparable
PostTrain Bench
Kimi K336.6%
Source
Step 3.7 Flash—
Not directly comparable
MLS-Bench Lite
Kimi K348.3%
Source
Step 3.7 Flash—
Not directly comparable
VulcanBench v3
Kimi K373.9%
Source
Step 3.7 Flash—
Not directly comparable
APEX-SWE
Kimi K348.0%
Source
Step 3.7 Flash—
Not directly comparable
EEBench
Kimi K338.3%
Source
Step 3.7 Flash—
Not directly comparable
InferenceEval
Kimi K339.8%
Source
Step 3.7 Flash—
Not directly comparable
KernelBench Internal
Kimi K368.5%
Source
Step 3.7 Flash—
Not directly comparable
SWE-bench Pro
Kimi K3—
Step 3.7 Flash56.3%
Source
Not directly comparable
Terminal-Bench 2.0
Kimi K3—
Step 3.7 Flash59.5%
Source
Not directly comparable

Knowledge

GPQA
Kimi K393.5%
Source
Step 3.7 Flash—
Not directly comparable
GPQA-D
Kimi K393.5%
Source
Step 3.7 Flash—
Not directly comparable
HLE
Kimi K356%
Source
Step 3.7 Flash—
Not directly comparable
HLE w/o tools
Kimi K343.5%
Source
Step 3.7 Flash—
Not directly comparable

Multimodal

OfficeQA Pro
Kimi K363.3%
Source
Step 3.7 Flash—
Not directly comparable
MMMU-Pro
Kimi K381.6%
Source
Step 3.7 Flash—
Not directly comparable
MMMU-Pro w/ Python
Kimi K383.4%
Source
Step 3.7 Flash—
Not directly comparable
CharXiv w/o tools
Kimi K384.8%
Source
Step 3.7 Flash—
Not directly comparable
CharXiv
Kimi K391.3%
Source
Step 3.7 Flash—
Not directly comparable
MathVision
Kimi K394.3%
Source
Step 3.7 Flash—
Not directly comparable
MathVision w/ Python
Kimi K397.8%
Source
Step 3.7 Flash—
Not directly comparable
BabyVision w/ Python
Kimi K385.7%
Source
Step 3.7 Flash—
Not directly comparable
ZeroBench
Kimi K323.0%
Source
Step 3.7 Flash—
Not directly comparable
ZeroBench w/ Python
Kimi K341.0%
Source
Step 3.7 Flash—
Not directly comparable
WorldVQA ForceAnswer
Kimi K351.0%
Source
Step 3.7 Flash—
Not directly comparable
OmniDocBench
Kimi K391.1%
Source
Step 3.7 Flash—
Not directly comparable
PerceptionBench
Kimi K358.5%
Source
Step 3.7 Flash—
Not directly comparable
SimpleVQA
Kimi K3—
Step 3.7 Flash79.2%
Source
Not directly comparable
V*
Kimi K3—
Step 3.7 Flash95.3%
Source
Not directly comparable

Frequently asked questions

Which is better, Kimi K3 or Step 3.7 Flash?

Kimi K3 has the higher public score, 80.53 versus 50.89, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Kimi K3 or Step 3.7 Flash?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Kimi K3 or Step 3.7 Flash?

Kimi K3 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, Kimi K3 or Step 3.7 Flash?

For the stated presets, chat costs $0.0105 on Kimi K3 and $0.00077 on Step 3.7 Flash; repository review costs $0.195 and $0.01345; the cache-heavy agent loop costs $0.27 and $0.0555. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Kimi K3 or Step 3.7 Flash?

Kimi K3 has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

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Last updated August 19, 2026

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Kimi K3 vs Step 3.7 Flash

Which one for your work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

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

Knowledge

Multimodal

Frequently asked questions

Which is better, Kimi K3 or Step 3.7 Flash?

Which is better for coding, Kimi K3 or Step 3.7 Flash?

Which is better for agentic tasks, Kimi K3 or Step 3.7 Flash?

Which costs less, Kimi K3 or Step 3.7 Flash?

Which has the larger context window, Kimi K3 or Step 3.7 Flash?

Related comparisons

Watch Kimi K3 vs Step 3.7 Flash