Kimi K3 vs o1

Updated October 2, 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 point estimate, 72.14 versus 40.08. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Kimi K3

Moonshot AI

72.14/100

Supported · Public rank #15

90% interval 68.8–75.5

Model B

OpenAI

40.08/100

Estimated · Public rank #129

Conditional range 25.7–54.4

Shared results: 1
Kimi K3 only: 47
o1 only: 3
Like-for-like categories: 1 / 8

Supported: Kimi K3 · Estimated: o1. Conditional ranges do not establish rank confidence.How 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.

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
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
O1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
O1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback

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.

61.4Kimi K330.3o1

Directional only · BenchAlign v5.8

Kimi K3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

GPQAKnowledge
Normalized gap 17.8
Kimi K3:93.5%
o1:75.7%
Kimi K3 source o1 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 v5.8 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.

Knowledge

Like-for-like

Kimi K3: 67.9; Supported · #18/171
o1: 40.8; Supported · #100/171
Basis: BenchAlign v5.8 lane · 6 vs 2 public rows
Reading: Kimi K3 leads

Coding

Directional only

Kimi K3: 61.4; Supported · #18/144
o1: 30.3; Estimated · #94/144
Basis: BenchAlign v5.8 lane · 14 vs 0 public rows
Reading: Directional only

Agentic

Not comparable

Kimi K3: 68.1; Supported · #8/119
o1: Not ranked
Basis: BenchAlign v5.8 lane · 12 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

Kimi K3: 65.8; #18/27
o1: 67.0; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Kimi K3: 89.4; #1/49
o1: Not ranked
Basis: Provisional lane · 3 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Kimi K3: Not ranked
o1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Kimi K3: Not ranked
o1: 84.5; #40/125
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Kimi K3: Not ranked
o1: 32.5; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Kimi K3 and o1
Category	Kimi K3	o1	Basis	Reading
Knowledge	67.9Supported · #18/171	40.8Supported · #100/171	Like-for-likeBenchAlign v5.8 lane · 6 vs 2 public rows	Kimi K3 leads
Coding	61.4Supported · #18/144	30.3Estimated · #94/144	Directional onlyBenchAlign v5.8 lane · 14 vs 0 public rows	Directional only
Agentic	68.1Supported · #8/119	Not ranked	Not comparableBenchAlign v5.8 lane · 12 vs 0 public rows	Not comparable
Reasoning	65.8#18/27	67.0Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	89.4#1/49	Not ranked	Not comparableProvisional lane · 3 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	84.5#40/125	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	32.5Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 1 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

Kimi K3: $0.0105; Fits in one request
o1: $0.045; 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

Kimi K3: $0.195; Fits in one request
o1: $0.93; 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

Kimi K3: $0.27; Fits in one request
o1: $3.90; Does not fit in one request; Cached input priced at the published list-input rate

o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.

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.

SpecificationKimi K3o1

Context window

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

Kimi K3

1.05M

200K

API model ID

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.

Kimi K3

$0.3 per 1M cached input tokens

Not published

Documented inputs

Kimi K3

Not sourced

Documented outputs

Kimi K3

Not sourced

Provider availability

Kimi K3

Not sourced

Reasoning profile

Kimi K3

Reasoning

Weight access

Kimi K3

Pending

Proprietary

License

Kimi K3

Pending

Proprietary

Release date

Kimi K3

2026-07-16

2024-12-01

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 point estimate, 72.14 versus 40.08. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: Repository review: $0.195 vs $0.93. Cache-heavy agent loop: $0.27 vs $3.90.
Context tradeoff: Kimi K3 has the larger documented window (1.05M).

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

Questions

Which is better, Kimi K3 or o1?

Kimi K3 has the higher public point estimate, 72.14 versus 40.08. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Kimi K3 or o1?

Kimi K3 scores higher for coding on the public lane, 61.4 to 30.3. O1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Kimi K3 or o1?

O1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Kimi K3 or o1?

For the stated presets, chat costs $0.0105 on Kimi K3 and $0.045 on o1; repository review costs $0.195 and $0.93; the cache-heavy agent loop costs $0.27 and $3.90. o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Kimi K3 or o1?

Kimi K3 has the larger documented context window: 1.05M, compared with 200K.

Benchmark evidence

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

Browse raw public benchmark evidence51 rows

Agentic

Terminal-Bench 2.1
Kimi K388.3%
Source
o1—
Not directly comparable
BrowseComp
Kimi K391.2%
Source
o1—
Not directly comparable
DeepSearchQA
Kimi K395.0%
Source
o1—
Not directly comparable
Toolathlon-Verified
Kimi K373.2%
Source
o1—
Not directly comparable
MCP Atlas
Kimi K384.2%
Source
o1—
Not directly comparable
AutomationBench
Kimi K330.8%
Source
o1—
Not directly comparable
JobBench
Kimi K352.9%
Source
o1—
Not directly comparable
APEX-Agents
Kimi K337.6%
Source
o1—
Not directly comparable
SpreadsheetBench 2
Kimi K334.8%
Source
o1—
Not directly comparable
DECK-Bench
Kimi K373.5%
Source
o1—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K380.9%
Source
o1—
Not directly comparable
ApprenticeBench
Kimi K318%
Source
o1—
Not directly comparable

Coding

DeepSWE
Kimi K367.5%
Source
o1—
Not directly comparable
CursorBench 3.2
Kimi K360.8%
Source
o1—
Not directly comparable
FrontierSWE
Kimi K381.2%
Source
o1—
Not directly comparable
ProgramBench
Kimi K377.8%
Source
o1—
Not directly comparable
Kimi Code Bench v2
Kimi K372.9%
Source
o1—
Not directly comparable
sweMarathon
Kimi K342%
Source
o1—
Not directly comparable
PostTrain Bench
Kimi K336.6%
Source
o1—
Not directly comparable
MLS-Bench Lite
Kimi K348.3%
Source
o1—
Not directly comparable
VulcanBench v3
Kimi K373.7%
Source
o1—
Not directly comparable
OpenHarmony Bench
Kimi K357.3%
Source
o1—
Not directly comparable
FrontierSWE v2
Kimi K325.9%
Source
o1—
Not directly comparable
LiveCodeBench (Vals)
Kimi K387.2%
Source
o1—
Not directly comparable
SWE-bench (Vals)
Kimi K393.4%
Source
o1—
Not directly comparable
PostTrainBench v1.1
Kimi K332.0%
Source
o1—
Not directly comparable

Reasoning

ARC-AGI-1
Kimi K394.50%
Source
o1—
Not directly comparable
ARC-AGI-2
Kimi K360.4%
Source
o1—
Not directly comparable

Multimodal

OfficeQA Pro
Kimi K363.3%
Source
o1—
Not directly comparable
MMMU-Pro
Kimi K381.6%
Source
o1—
Not directly comparable
MMMU-Pro w/ Python
Kimi K383.4%
Source
o1—
Not directly comparable
CharXiv w/o tools
Kimi K384.8%
Source
o1—
Not directly comparable
CharXiv
Kimi K391.3%
Source
o1—
Not directly comparable
MathVision
Kimi K394.3%
Source
o1—
Not directly comparable
MathVision w/ Python
Kimi K397.8%
Source
o1—
Not directly comparable
BabyVision w/ Python
Kimi K385.7%
Source
o1—
Not directly comparable
ZeroBench
Kimi K323.0%
Source
o1—
Not directly comparable
ZeroBench w/ Python
Kimi K341.0%
Source
o1—
Not directly comparable
WorldVQA ForceAnswer
Kimi K351.0%
Source
o1—
Not directly comparable
OmniDocBench
Kimi K391.1%
Source
o1—
Not directly comparable
PerceptionBench
Kimi K358.5%
Source
o1—
Not directly comparable

Knowledge

GPQA
Kimi K393.5%
Source
o175.7%
Source
Kimi K3 leads this result
GPQA-D
Kimi K393.5%
Source
o1—
Not directly comparable
HLE
Kimi K356%
Source
o1—
Not directly comparable
HLE w/o tools
Kimi K343.5%
Source
o1—
Not directly comparable
GPQA Diamond (Vals)
Kimi K392.9%
Source
o1—
Not directly comparable
MMLU-Pro (Vals)
Kimi K388.0%
Source
o1—
Not directly comparable
MMLU
Kimi K3—
o191.8%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Kimi K352.7%
Source
o1—
Not directly comparable
IFEval
Kimi K3—
o192.2%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Kimi K3—
o19.310%
Source
Not directly comparable

51 public results · 1 shared

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Last updated October 2, 2026

Kimi K3 vs o1

Decision reading

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Coding

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, Kimi K3 or o1?

Which is better for coding, Kimi K3 or o1?

Which is better for agentic tasks, Kimi K3 or o1?

Which costs less, Kimi K3 or o1?

Which has the larger context window, Kimi K3 or o1?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Kimi K3 vs o1