Model A

OpenAI

79.64/100

Supported · Public rank #6

90% interval 77.5–81.8

GPT-5.6 Sol vs Kimi K3

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Kimi K3

Moonshot AI

74.87/100

Supported · Public rank #8

90% interval 71.4–78.3

Decision reading

GPT-5.6 Sol has the higher public score estimate, 79.64 versus 74.87, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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

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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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 74.4 to 68, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Kimi K3
Kimi K3 leads on the public agentic lane, 71.9 to 70.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Kimi K3
Kimi K3 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
Kimi K3
Kimi K3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Kimi K3
Kimi K3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented

What is actually comparable

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

20 GPT-5.6 Sol only

15 Shared

28 Kimi K3 only

Shared results: 15
GPT-5.6 Sol only: 20
Kimi K3 only: 28
Like-for-like categories: 3 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Like-for-like

GPT-5.6 Sol: 70.1; Supported · #6/151
Kimi K3: 71.9; Supported · #4/151
Basis: BenchAlign lane · 8 vs 11 public rows
Reading: Kimi K3 leads · intervals overlap

Coding

Like-for-like

GPT-5.6 Sol: 74.4; Supported · #5/183
Kimi K3: 68.0; Supported · #7/183
Basis: BenchAlign lane · 11 vs 13 public rows
Reading: GPT-5.6 Sol leads · intervals overlap

Knowledge

Like-for-like

GPT-5.6 Sol: 80.4; Supported · #5/181
Kimi K3: 72.3; Supported · #8/181
Basis: BenchAlign lane · 8 vs 6 public rows
Reading: GPT-5.6 Sol leads · intervals overlap

Reasoning

Directional only

GPT-5.6 Sol: 69.8; #14/22
Kimi K3: 78.5; #3/22
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Directional only

Multimodal

Directional only

GPT-5.6 Sol: 87.5; #4/48
Kimi K3: 89.5; #1/48
Basis: Provisional lane · 1 vs 3 weighted rows
Reading: Directional only

Math

Not comparable

GPT-5.6 Sol: 97.0; Unranked · 3 rankable rows
Kimi K3: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.6 Sol: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.6 Sol: 88.8; #27/120
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.6 Sol and Kimi K3
Category	GPT-5.6 Sol	Kimi K3	Basis	Reading
Agentic	70.1Supported · #6/151	71.9Supported · #4/151	Like-for-likeBenchAlign lane · 8 vs 11 public rows	Kimi K3 leads · intervals overlap
Coding	74.4Supported · #5/183	68.0Supported · #7/183	Like-for-likeBenchAlign lane · 11 vs 13 public rows	GPT-5.6 Sol leads · intervals overlap
Knowledge	80.4Supported · #5/181	72.3Supported · #8/181	Like-for-likeBenchAlign lane · 8 vs 6 public rows	GPT-5.6 Sol leads · intervals overlap
Reasoning	69.8#14/22	78.5#3/22	Directional onlyProvisional lane · 2 vs 0 weighted rows	Directional only
Multimodal	87.5#4/48	89.5#1/48	Directional onlyProvisional lane · 1 vs 3 weighted rows	Directional only
Math	97.0Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	88.8#27/120	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.

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.

cursorBench32
Coding
GPT-5.6 Sol: 67.2%Kimi K3: 60.8%Normalized gap 6.4Shared source
LiveCodeBench (Vals)
Coding
GPT-5.6 Sol: 82.6%Kimi K3: 87.2%Normalized gap 4.6GPT-5.6 Sol source Kimi K3 source
Terminal-Bench 2.0
Agentic
GPT-5.6 Sol: 91.9%Kimi K3: 88.3%Normalized gap 3.6GPT-5.6 Sol source Kimi K3 source
MMMU-Pro
Multimodal
GPT-5.6 Sol: 83%Kimi K3: 81.6%Normalized gap 1.4GPT-5.6 Sol source Kimi K3 source
GPQA
Knowledge
GPT-5.6 Sol: 94.6%Kimi K3: 93.5%Normalized gap 1.1GPT-5.6 Sol source Kimi K3 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

GPT-5.6 Sol: $0.02; Fits in one request
Kimi K3: $0.0105; Fits in one request

Kimi K3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Sol: $0.34; Fits in one request
Kimi K3: $0.195; Fits in one request

Kimi K3 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-5.6 Sol: $0.5; Fits in one request
Kimi K3: $0.27; Fits in one request

Kimi K3 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.

SpecificationGPT-5.6 SolKimi K3

Context window

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

GPT-5.6 Sol

1.05M

OpenAI model catalog

Kimi K3

1.05M

API model ID

GPT-5.6 Sol

gpt-5.6-sol

OpenAI model catalog

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.

GPT-5.6 Sol

$0.5 per 1M cached input tokens

OpenAI pricing

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

GPT-5.6 Sol

text, image

OpenAI model catalog

Kimi K3

Not sourced

Documented outputs

GPT-5.6 Sol

text

OpenAI model catalog

Kimi K3

Not sourced

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Kimi K3

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

Kimi K3

Reasoning

Weight access

GPT-5.6 Sol

Proprietary

Kimi K3

Pending

License

GPT-5.6 Sol

Proprietary

Kimi K3

Pending

Release date

GPT-5.6 Sol

2026-07-09

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: GPT-5.6 Sol has the higher public score estimate, 79.64 versus 74.87, but the 90% score intervals overlap.
Workload cost: Repository review: $0.34 vs $0.195. Cache-heavy agent loop: $0.5 vs $0.27.
Context tradeoff: Both models list 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 evidence63 rows

Agentic

Terminal-Bench 3.0
GPT-5.6 Sol34.6%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%
Source
Kimi K388.3%
Source
GPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%
Source
Kimi K391.2%
Source
GPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%
Source
Kimi K3—
Not directly comparable
CyberGym
GPT-5.6 Sol84.5%
Source
Kimi K3—
Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%
Source
Kimi K3—
Not directly comparable
Toolathlon
GPT-5.6 Sol58%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%
Source
Kimi K380.9%
Source
GPT-5.6 Sol leads this result
DeepSearchQA
GPT-5.6 Sol—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
GPT-5.6 Sol—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
GPT-5.6 Sol—
Kimi K384.2%
Source
Not directly comparable
AutomationBench
GPT-5.6 Sol—
Kimi K330.8%
Source
Not directly comparable
JobBench
GPT-5.6 Sol—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
GPT-5.6 Sol—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
GPT-5.6 Sol—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
GPT-5.6 Sol—
Kimi K373.5%
Source
Not directly comparable

Coding

Bug Hunt Bench
GPT-5.6 Sol42 fixes
Source
Kimi K3—
Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol91.9%
Source
Kimi K3—
Not directly comparable
deepSwe
GPT-5.6 Sol72.7%
Source
Kimi K367.5%
Source
GPT-5.6 Sol leads this result
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%
Source
Kimi K3—
Not directly comparable
FrontierSWE v2
Shared source
GPT-5.6 Sol32.2%
Kimi K325.9%
GPT-5.6 Sol leads this result
cursorBench32
Shared source
GPT-5.6 Sol67.2%
Kimi K360.8%
GPT-5.6 Sol leads this result
VulcanBench v3
GPT-5.6 Sol87.0%
Source
Kimi K373.7%
Source
GPT-5.6 Sol leads this result
VulcanBench CII v1
GPT-5.6 Sol86.5%
Source
Kimi K3—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%
Source
Kimi K387.2%
Source
Kimi K3 leads this result
SWE-bench (Vals)
GPT-5.6 Sol96.2%
Source
Kimi K393.4%
Source
GPT-5.6 Sol leads this result
FrontierSWE
GPT-5.6 Sol—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
GPT-5.6 Sol—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
GPT-5.6 Sol—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
GPT-5.6 Sol—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
GPT-5.6 Sol—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
GPT-5.6 Sol—
Kimi K348.3%
Source
Not directly comparable
OpenHarmony Bench
GPT-5.6 Sol—
Kimi K357.3%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.6 Sol92.5%
Source
Kimi K3—
Not directly comparable
ARC-AGI-3
GPT-5.6 Sol7.8%
Source
Kimi K3—
Not directly comparable
GeneBench-Pro
GPT-5.6 Sol28.7%
Source
Kimi K3—
Not directly comparable

Knowledge

GPQA
GPT-5.6 Sol94.6%
Source
Kimi K393.5%
Source
GPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%
Source
Kimi K393.5%
Source
GPT-5.6 Sol leads this result
HLE-Verified
GPT-5.6 Sol54.5%
Source
Kimi K3—
Not directly comparable
LABBench2
GPT-5.6 Sol82.1%
Source
Kimi K3—
Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%
Source
Kimi K3—
Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%
Source
Kimi K3—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%
Source
Kimi K392.9%
Source
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%
Source
Kimi K388.0%
Source
GPT-5.6 Sol leads this result
HLE
GPT-5.6 Sol—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
GPT-5.6 Sol—
Kimi K343.5%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.6 Sol89%
Source
Kimi K3—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%
Source
Kimi K3—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%
Source
Kimi K3—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.6 Sol83%
Source
Kimi K381.6%
Source
GPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%
Source
Kimi K383.4%
Source
GPT-5.6 Sol leads this result
OfficeQA Pro
GPT-5.6 Sol—
Kimi K363.3%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.6 Sol—
Kimi K384.8%
Source
Not directly comparable
CharXiv
GPT-5.6 Sol—
Kimi K391.3%
Source
Not directly comparable
MathVision
GPT-5.6 Sol—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
GPT-5.6 Sol—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
GPT-5.6 Sol—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
GPT-5.6 Sol—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
GPT-5.6 Sol—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
GPT-5.6 Sol—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
GPT-5.6 Sol—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
GPT-5.6 Sol—
Kimi K358.5%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Sol or Kimi K3?

GPT-5.6 Sol has the higher public score estimate, 79.64 versus 74.87, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.6 Sol or Kimi K3?

GPT-5.6 Sol leads the public coding lane, 74.4 to 68, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-5.6 Sol or Kimi K3?

Kimi K3 leads the public agentic tasks lane, 71.9 to 70.1, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, GPT-5.6 Sol or Kimi K3?

For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.0105 on Kimi K3; repository review costs $0.34 and $0.195; the cache-heavy agent loop costs $0.5 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Sol or Kimi K3?

Both models list the same context window, 1.05M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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GPT-5.6 Sol vs Kimi K3

Which one for your work

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Long documents

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Math

Multilingual

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

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GPT-5.6 Sol or Kimi K3?

Which is better for coding, GPT-5.6 Sol or Kimi K3?

Which is better for agentic tasks, GPT-5.6 Sol or Kimi K3?

Which costs less, GPT-5.6 Sol or Kimi K3?

Which has the larger context window, GPT-5.6 Sol or Kimi K3?

Related comparisons

Watch GPT-5.6 Sol vs Kimi K3