GPT-6 Cyber vs Kimi K3

Updated November 5, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

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.

Model A

GPT-6 Cyber

OpenAI

—

Evidence status unavailable

90% interval unavailable

Model B

Kimi K3

Moonshot AI

69.46/100

Supported · Public rank #17

90% interval 65.2–73.7

Shared results: 0
GPT-6 Cyber only: 1
Kimi K3 only: 58
Like-for-like categories: 0 / 8

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.

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
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

Show secondary and unsupported calls

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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-6 Cyber 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
GPT-6 Cyber 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.

—GPT-6 Cyber58.6Kimi K3

Not comparable · BenchAlign v5.8

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

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.

A shared-evidence shape is not available.

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

GPT-6 Cyber: Not ranked
Kimi K3: 68.0; Supported · #14/135
Basis: BenchAlign v5.8 lane · 1 vs 18 public rows
Reading: Not comparable

Coding

Not comparable

GPT-6 Cyber: Not ranked
Kimi K3: 58.6; Supported · #20/156
Basis: BenchAlign v5.8 lane · 0 vs 17 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-6 Cyber: Not ranked
Kimi K3: 69.7; #15/26
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-6 Cyber: Not ranked
Kimi K3: 90.0; #6/58
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Knowledge

Not comparable

GPT-6 Cyber: Not ranked
Kimi K3: 67.4; Supported · #23/177
Basis: BenchAlign v5.8 lane · 0 vs 6 public rows
Reading: Not comparable

Multilingual

Not comparable

GPT-6 Cyber: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-6 Cyber: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-6 Cyber: 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 Cyber and Kimi K3
Category	GPT-6 Cyber	Kimi K3	Basis	Reading
Agentic	Not ranked	68.0Supported · #14/135	Not comparableBenchAlign v5.8 lane · 1 vs 18 public rows	Not comparable
Coding	Not ranked	58.6Supported · #20/156	Not comparableBenchAlign v5.8 lane · 0 vs 17 public rows	Not comparable
Reasoning	Not ranked	69.7#15/26	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	90.0#6/58	Not comparableProvisional lane · 0 vs 3 weighted rows	Not comparable
Knowledge	Not ranked	67.4Supported · #23/177	Not comparableBenchAlign v5.8 lane · 0 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 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.

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 Cyber: $0.07; 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-6 Cyber: $1.30; 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-6 Cyber: $1.80; 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.

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 CyberKimi K3

Context window

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

GPT-6 Cyber

1.05M

OpenAI GPT-6-Cyber model documentation

Kimi K3

1.05M

API model ID

GPT-6 Cyber

gpt-6-cyber

OpenAI GPT-6-Cyber 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 Cyber

$2 per 1M cached input tokens

OpenAI GPT-6-Cyber model documentation

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

GPT-6 Cyber

text, image

OpenAI GPT-6-Cyber model documentation

Kimi K3

Not sourced

Documented outputs

GPT-6 Cyber

text

OpenAI GPT-6-Cyber model documentation

Kimi K3

Not sourced

Provider availability

GPT-6 Cyber

Limited Access · Daybreak Red (OpenAI API), Daybreak Red (Codex)

OpenAI: GPT-6 Cyber

Kimi K3

Not sourced

Reasoning profile

GPT-6 Cyber

Reasoning

Kimi K3

Reasoning

Weight access

GPT-6 Cyber

Proprietary

Kimi K3

Open Weight

License

GPT-6 Cyber

Proprietary

Kimi K3

Open Weight

Release date

GPT-6 Cyber

2026-11-05

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: Repository review: $1.30 vs $0.195. Cache-heavy agent loop: $1.80 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 Cyber or Kimi K3?

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.

Which is better for coding, GPT-6 Cyber or Kimi K3?

GPT-6 Cyber is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-6 Cyber or Kimi K3?

GPT-6 Cyber is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-6 Cyber or Kimi K3?

For the stated presets, chat costs $0.07 on GPT-6 Cyber and $0.0105 on Kimi K3; repository review costs $1.30 and $0.195; the cache-heavy agent loop costs $1.80 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, GPT-6 Cyber or Kimi K3?

Both models list the same context window, 1.05M.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-6 Cyber

API / mo$90,000

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Kimi K3

API / mo$13,500

Self-host / mo$57,606

Break-even291M/day

Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence59 rows

Agentic

ExploitGym
GPT-6 Cyber48.5%
Source
Kimi K3—
Not directly comparable
Finch
GPT-6 Cyber—
Kimi K377.3%
Source
Not directly comparable
b3 Agent Security
GPT-6 Cyber—
Kimi K388.0%
Source
Not directly comparable
Cyber Misuse Refusal
GPT-6 Cyber—
Kimi K394.0%
Source
Not directly comparable
Terminal-Bench 2.1
GPT-6 Cyber—
Kimi K388.3%
Source
Not directly comparable
BrowseComp
GPT-6 Cyber—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
GPT-6 Cyber—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
GPT-6 Cyber—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
GPT-6 Cyber—
Kimi K384.2%
Source
Not directly comparable
AutomationBench
GPT-6 Cyber—
Kimi K330.8%
Source
Not directly comparable
JobBench
GPT-6 Cyber—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
GPT-6 Cyber—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
GPT-6 Cyber—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
GPT-6 Cyber—
Kimi K373.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6 Cyber—
Kimi K380.9%
Source
Not directly comparable
ApprenticeBench
GPT-6 Cyber—
Kimi K318%
Source
Not directly comparable
Hermes Index
GPT-6 Cyber—
Kimi K339.2%
Source
Not directly comparable
Hermes Bench
GPT-6 Cyber—
Kimi K371.5%
Source
Not directly comparable
MazeBench
GPT-6 Cyber—
Kimi K31%
Source
Not directly comparable

Coding

ScarfBench
GPT-6 Cyber—
Kimi K361.3%
Source
Not directly comparable
DeepSWE
GPT-6 Cyber—
Kimi K367.5%
Source
Not directly comparable
CursorBench 3.2
GPT-6 Cyber—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
GPT-6 Cyber—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
GPT-6 Cyber—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
GPT-6 Cyber—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
GPT-6 Cyber—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
GPT-6 Cyber—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
GPT-6 Cyber—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
GPT-6 Cyber—
Kimi K373.7%
Source
Not directly comparable
OpenHarmony Bench
GPT-6 Cyber—
Kimi K357.3%
Source
Not directly comparable
FrontierSWE v2
GPT-6 Cyber—
Kimi K325.9%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-6 Cyber—
Kimi K387.2%
Source
Not directly comparable
SWE-bench (Vals)
GPT-6 Cyber—
Kimi K393.4%
Source
Not directly comparable
PostTrainBench v1.1
GPT-6 Cyber—
Kimi K332.0%
Source
Not directly comparable
Bug Hunt Bench
GPT-6 Cyber—
Kimi K321.0 fixes
Source
Not directly comparable
FrontierCode 2.0
GPT-6 Cyber—
Kimi K317.7%
Source
Not directly comparable

Reasoning

ARC-AGI-1
GPT-6 Cyber—
Kimi K394.50%
Source
Not directly comparable
ARC-AGI-2
GPT-6 Cyber—
Kimi K360.4%
Source
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
GPT-6 Cyber—
Kimi K383.17%
Source
Not directly comparable
OfficeQA Pro
GPT-6 Cyber—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
GPT-6 Cyber—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
GPT-6 Cyber—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
GPT-6 Cyber—
Kimi K384.8%
Source
Not directly comparable
CharXiv
GPT-6 Cyber—
Kimi K391.3%
Source
Not directly comparable
MathVision
GPT-6 Cyber—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
GPT-6 Cyber—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
GPT-6 Cyber—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
GPT-6 Cyber—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
GPT-6 Cyber—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
GPT-6 Cyber—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
GPT-6 Cyber—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
GPT-6 Cyber—
Kimi K358.5%
Source
Not directly comparable

Knowledge

GPQA
GPT-6 Cyber—
Kimi K393.5%
Source
Not directly comparable
GPQA-D
GPT-6 Cyber—
Kimi K393.5%
Source
Not directly comparable
HLE
GPT-6 Cyber—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
GPT-6 Cyber—
Kimi K343.5%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-6 Cyber—
Kimi K392.9%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-6 Cyber—
Kimi K388.0%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GPT-6 Cyber—
Kimi K352.7%
Source
Not directly comparable

59 public results · 0 shared

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Last updated November 5, 2026

GPT-6 Cyber vs Kimi K3

Decision reading

Which one for your work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

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

Agentic

Coding

Reasoning

Multimodal

Knowledge

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 Cyber or Kimi K3?

Which is better for coding, GPT-6 Cyber or Kimi K3?

Which is better for agentic tasks, GPT-6 Cyber or Kimi K3?

Which costs less, GPT-6 Cyber or Kimi K3?

Which has the larger context window, GPT-6 Cyber or Kimi K3?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch GPT-6 Cyber vs Kimi K3