GPT-6.1 Sol vs Kimi K3

Updated September 29, 2026. Rank says Kimi K3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Kimi K3 has the higher public score estimate, 71.57 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled 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

GPT-6.1 Sol

OpenAI

66.86/100

Estimated · Public rank #23

90% interval 55.4–78.4

Model B

Kimi K3

Moonshot AI

71.57/100

Supported · Public rank #13

90% interval 68.4–74.8

Shared results: 2
GPT-6.1 Sol only: 7
Kimi K3 only: 44
Like-for-like categories: 1 / 8

Estimated: GPT-6.1 Sol · Supported: Kimi K3How 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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-6.1 Sol
GPT-6.1 Sol leads on the public coding lane, 67 to 61.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
GPT-6.1 Sol
GPT-6.1 Sol 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
GPT-6.1 Sol
GPT-6.1 Sol 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
GPT-6.1 Sol
GPT-6.1 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented

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.

67.0GPT-6.1 Sol61.6Kimi K3

Like-for-like · BenchAlign v5.7

GPT-6.1 Sol leads the like-for-like coding row, although the 90% intervals overlap.

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

Same basis rules as the category table below

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.

AutomationBenchAgentic
Normalized gap 5.3
GPT-6.1 Sol:36.1%
Kimi K3:30.8%
GPT-6.1 Sol source Kimi K3 source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Coding

Like-for-like

GPT-6.1 Sol: 67.0; Supported · #8/143
Kimi K3: 61.6; Supported · #17/143
Basis: BenchAlign v5.7 lane · 1 vs 13 public rows
Reading: GPT-6.1 Sol leads · intervals overlap

Knowledge

Directional only

GPT-6.1 Sol: 71.3; Estimated · #10/169
Kimi K3: 68.1; Supported · #16/169
Basis: BenchAlign v5.7 lane · 5 vs 6 public rows
Reading: Directional only

Agentic

Not comparable

GPT-6.1 Sol: Not ranked
Kimi K3: 69.0; Supported · #6/117
Basis: BenchAlign v5.7 lane · 3 vs 12 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-6.1 Sol: 79.4; Unranked · 2 rankable rows
Kimi K3: 65.6; #17/27
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-6.1 Sol: 83.9; Unranked · 1 rankable row
Kimi K3: 89.4; #1/50
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-6.1 Sol: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-6.1 Sol: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-6.1 Sol: 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 GPT-6.1 Sol and Kimi K3
Category	GPT-6.1 Sol	Kimi K3	Basis	Reading
Coding	67.0Supported · #8/143	61.6Supported · #17/143	Like-for-likeBenchAlign v5.7 lane · 1 vs 13 public rows	GPT-6.1 Sol leads · intervals overlap
Knowledge	71.3Estimated · #10/169	68.1Supported · #16/169	Directional onlyBenchAlign v5.7 lane · 5 vs 6 public rows	Directional only
Agentic	Not ranked	69.0Supported · #6/117	Not comparableBenchAlign v5.7 lane · 3 vs 12 public rows	Not comparable
Reasoning	79.4Unranked · 2 rankable rows	65.6#17/27	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	83.9Unranked · 1 rankable row	89.4#1/50	Not comparableProvisional lane · 0 vs 3 weighted 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 v5.7) 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

GPT-6.1 Sol: $0.007; Fits in one request
Kimi K3: $0.0105; Fits in one request

GPT-6.1 Sol has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-6.1 Sol: $0.13; Fits in one request
Kimi K3: $0.195; Fits in one request

GPT-6.1 Sol 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-6.1 Sol: $0.16; Fits in one request
Kimi K3: $0.27; Fits in one request

GPT-6.1 Sol has the lower modeled cost

Costs use the listed standard API rates.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

SpecificationGPT-6.1 SolKimi K3

Context window

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

GPT-6.1 Sol

1.05M

OpenAI GPT-6.1 Sol model documentation

Kimi K3

1.05M

API model ID

GPT-6.1 Sol

gpt-6.1-sol

OpenAI GPT-6.1 Sol 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.

GPT-6.1 Sol

$0.1 per 1M cached input tokens

OpenAI GPT-6.1 Sol model documentation

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

GPT-6.1 Sol

text, image

OpenAI GPT-6.1 Sol model documentation

Kimi K3

Not sourced

Documented outputs

GPT-6.1 Sol

text

OpenAI GPT-6.1 Sol model documentation

Kimi K3

Not sourced

Provider availability

GPT-6.1 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API

OpenAI GPT-6.1 Sol model documentation

Kimi K3

Not sourced

Reasoning profile

GPT-6.1 Sol

Reasoning

Kimi K3

Reasoning

Weight access

GPT-6.1 Sol

Proprietary

Kimi K3

Pending

License

GPT-6.1 Sol

Proprietary

Kimi K3

Pending

Release date

GPT-6.1 Sol

2026-09-29

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: Kimi K3 has the higher public score estimate, 71.57 versus 66.86, but the 90% score intervals overlap.
Workload cost: Repository review: $0.13 vs $0.195. Cache-heavy agent loop: $0.16 vs $0.27.
Context tradeoff: Both models list 1.05M.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-6.1 Sol or Kimi K3?

Kimi K3 has the higher public score estimate, 71.57 versus 66.86, 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-6.1 Sol or Kimi K3?

GPT-6.1 Sol leads the public coding lane, 67 to 61.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-6.1 Sol or Kimi K3?

GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-6.1 Sol or Kimi K3?

For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.0105 on Kimi K3; repository review costs $0.13 and $0.195; the cache-heavy agent loop costs $0.16 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, GPT-6.1 Sol or Kimi K3?

Both models list the same context window, 1.05M.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence53 rows

Agentic

AutomationBench
GPT-6.1 Sol36.1%
Source
Kimi K330.8%
Source
GPT-6.1 Sol leads this result
Terminal-Bench-Science 0.1
GPT-6.1 Sol57.0%
Source
Kimi K3—
Not directly comparable
ExploitGym
GPT-6.1 Sol35.1%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 2.1
GPT-6.1 Sol—
Kimi K388.3%
Source
Not directly comparable
BrowseComp
GPT-6.1 Sol—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
GPT-6.1 Sol—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
GPT-6.1 Sol—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
GPT-6.1 Sol—
Kimi K384.2%
Source
Not directly comparable
JobBench
GPT-6.1 Sol—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
GPT-6.1 Sol—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
GPT-6.1 Sol—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
GPT-6.1 Sol—
Kimi K373.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6.1 Sol—
Kimi K380.9%
Source
Not directly comparable
ApprenticeBench
GPT-6.1 Sol—
Kimi K318%
Source
Not directly comparable

Coding

DeepSWE
GPT-6.1 Sol71.9%
Source
Kimi K367.5%
Source
GPT-6.1 Sol leads this result
cursorBench32
GPT-6.1 Sol—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
GPT-6.1 Sol—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
GPT-6.1 Sol—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
GPT-6.1 Sol—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
GPT-6.1 Sol—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
GPT-6.1 Sol—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
GPT-6.1 Sol—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
GPT-6.1 Sol—
Kimi K373.7%
Source
Not directly comparable
OpenHarmony Bench
GPT-6.1 Sol—
Kimi K357.3%
Source
Not directly comparable
FrontierSWE v2
GPT-6.1 Sol—
Kimi K325.9%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-6.1 Sol—
Kimi K387.2%
Source
Not directly comparable
SWE-bench (Vals)
GPT-6.1 Sol—
Kimi K393.4%
Source
Not directly comparable

Reasoning

ARC-AGI-1
GPT-6.1 Sol—
Kimi K394.50%
Source
Not directly comparable
ARC-AGI-2
GPT-6.1 Sol—
Kimi K360.4%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GPT-6.1 Sol—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
GPT-6.1 Sol—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
GPT-6.1 Sol—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
GPT-6.1 Sol—
Kimi K384.8%
Source
Not directly comparable
CharXiv
GPT-6.1 Sol—
Kimi K391.3%
Source
Not directly comparable
MathVision
GPT-6.1 Sol—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
GPT-6.1 Sol—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
GPT-6.1 Sol—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
GPT-6.1 Sol—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
GPT-6.1 Sol—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
GPT-6.1 Sol—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
GPT-6.1 Sol—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
GPT-6.1 Sol—
Kimi K358.5%
Source
Not directly comparable

Knowledge

HealthBench (raw)
GPT-6.1 Sol56.7%
Source
Kimi K3—
Not directly comparable
HealthBench (length-adjusted)
GPT-6.1 Sol58.5%
Source
Kimi K3—
Not directly comparable
HealthBench Professional
GPT-6.1 Sol64.2%
Source
Kimi K3—
Not directly comparable
HealthBench Professional (raw)
GPT-6.1 Sol67.2%
Source
Kimi K3—
Not directly comparable
HealthBench Hard
GPT-6.1 Sol36.2%
Source
Kimi K3—
Not directly comparable
GPQA
GPT-6.1 Sol—
Kimi K393.5%
Source
Not directly comparable
GPQA-D
GPT-6.1 Sol—
Kimi K393.5%
Source
Not directly comparable
HLE
GPT-6.1 Sol—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
GPT-6.1 Sol—
Kimi K343.5%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-6.1 Sol—
Kimi K392.9%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-6.1 Sol—
Kimi K388.0%
Source
Not directly comparable

53 public results · 2 shared

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Last updated September 29, 2026

GPT-6.1 Sol vs Kimi K3

Decision reading

Which one for your work

Coding work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Agentic work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

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

If you already use one of these models

Questions

Which is better, GPT-6.1 Sol or Kimi K3?

Which is better for coding, GPT-6.1 Sol or Kimi K3?

Which is better for agentic tasks, GPT-6.1 Sol or Kimi K3?

Which costs less, GPT-6.1 Sol or Kimi K3?

Which has the larger context window, GPT-6.1 Sol or Kimi K3?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Related comparisons

Watch GPT-6.1 Sol vs Kimi K3