Gemini 4 Argon vs Kimi K3

Updated November 13, 2026. Rank says Gemini 4 Argon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 4 Argon has the higher public score, 79.03 versus 68.42, and the 90% score intervals do not overlap. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 4 Argon

Google

79.03/100

Supported · Public rank #8

90% interval 75.4–82.7

Model B

Kimi K3

Moonshot AI

68.42/100

Supported · Public rank #17

90% interval 64.0–72.9

API price per 1M tokens

Gemini 4 Argon: $2 input / $10 output

Kimi K3: $3 input / $15 output

Context window

Gemini 4 Argon: 1M

Kimi K3: 1.05M

Released

Gemini 4 Argon: September 30, 2026

Kimi K3: July 16, 2026

Shared results: 13
Gemini 4 Argon only: 26
Kimi K3 only: 46
Like-for-like categories: 4 / 8

Supported: Gemini 4 Argon and 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
Gemini 4 Argon
Gemini 4 Argon has the higher public coding point estimate, 71.8 to 56.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Gemini 4 Argon
Gemini 4 Argon has the higher public agentic point estimate, 74.9 to 65.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Chat turn cost
1K fresh input + 500 output tokens
Gemini 4 Argon
Gemini 4 Argon 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
Gemini 4 Argon
Gemini 4 Argon 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
Gemini 4 Argon
Gemini 4 Argon has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

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.

71.8Gemini 4 Argon56.2Kimi K3

Like-for-like · BenchAlign v5.8

Gemini 4 Argon 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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

What do you need a model for?

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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.

ARC-AGI-2Reasoning
Normalized gap 30.8
Gemini 4 Argon:91.3%
Kimi K3:60.4%
Gemini 4 Argon source Kimi K3 source
AutomationBenchAgentic
Normalized gap 20.5
Gemini 4 Argon:51.3%
Kimi K3:30.8%
Gemini 4 Argon 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 v5.8 lane for agentic, coding, reasoning, multimodal, 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

Gemini 4 Argon: 74.9; Supported · #6/135
Kimi K3: 65.7; Supported · #16/135
Basis: BenchAlign v5.8 lane · 13 vs 18 public rows
Reading: Gemini 4 Argon leads · intervals overlap

Coding

Like-for-like

Gemini 4 Argon: 71.8; Supported · #9/162
Kimi K3: 56.2; Supported · #24/162
Basis: BenchAlign v5.8 lane · 9 vs 18 public rows
Reading: Gemini 4 Argon leads

Reasoning

Like-for-like

Gemini 4 Argon: 76.5; Supported · #7/160
Kimi K3: 65.5; Supported · #21/160
Basis: BenchAlign v5.8 lane · 7 vs 2 public rows
Reading: Gemini 4 Argon leads · intervals overlap

Knowledge

Like-for-like

Gemini 4 Argon: 79.2; Supported · #8/180
Kimi K3: 67.7; Supported · #20/180
Basis: BenchAlign v5.8 lane · 2 vs 6 public rows
Reading: Gemini 4 Argon leads · intervals overlap

Multimodal

Not comparable

Gemini 4 Argon: Not ranked
Kimi K3: 67.7; Estimated · #19/67
Basis: BenchAlign v5.8 lane · 5 vs 14 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 4 Argon: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 4 Argon: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 4 Argon: 87.4; Unranked · 2 rankable rows
Kimi K3: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 4 Argon and Kimi K3
Category	Gemini 4 Argon	Kimi K3	Basis	Reading
Agentic	74.9Supported · #6/135	65.7Supported · #16/135	Like-for-likeBenchAlign v5.8 lane · 13 vs 18 public rows	Gemini 4 Argon leads · intervals overlap
Coding	71.8Supported · #9/162	56.2Supported · #24/162	Like-for-likeBenchAlign v5.8 lane · 9 vs 18 public rows	Gemini 4 Argon leads
Reasoning	76.5Supported · #7/160	65.5Supported · #21/160	Like-for-likeBenchAlign v5.8 lane · 7 vs 2 public rows	Gemini 4 Argon leads · intervals overlap
Knowledge	79.2Supported · #8/180	67.7Supported · #20/180	Like-for-likeBenchAlign v5.8 lane · 2 vs 6 public rows	Gemini 4 Argon leads · intervals overlap
Multimodal	Not ranked	67.7Estimated · #19/67	Not comparableBenchAlign v5.8 lane · 5 vs 14 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	87.4Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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

Gemini 4 Argon: $0.007; Fits in one request
Kimi K3: $0.0105; Fits in one request

Gemini 4 Argon has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 4 Argon: $0.13; Fits in one request
Kimi K3: $0.195; Fits in one request

Gemini 4 Argon has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Gemini 4 Argon: $0.16; Fits in one request
Kimi K3: $0.27; Fits in one request

Gemini 4 Argon has the lower modeled cost

Costs use the listed standard API rates.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Input, millionsOutput, millionsCached input, %

Gemini 4 Argon: $40.00; per month
Kimi K3: $60.00; per month

Standard listed rates, no batch or volume discounts.

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

Price against score, among all priced models

Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.

Gemini 4 ArgonKimi K3

2 models at the same price score higher than Gemini 4 Argon. The nearest is Gemini 4 Carbon (score 89.2, also $10.00 per 1M output tokens).
4 cheaper models score at least as high as Kimi K3. The nearest is Gemini 4 Carbon (score 89.2, $10.00 per 1M output tokens).

Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.

See Gemini 4 Argon on the full price vs performance chart

Specification differences

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

SpecificationGemini 4 ArgonKimi K3

Context window

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

Gemini 4 Argon

Google Gemini 4 Argon model documentation

Kimi K3

1.05M

API model ID

Gemini 4 Argon

gemini-4-argon

Google Gemini 4 Argon 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.

Gemini 4 Argon

$0.1 per 1M cached input tokens

Google Gemini API pricing

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Gemini 4 Argon

text, image, video, audio, pdf

Google Gemini 4 Argon model documentation

Kimi K3

text, image, video

Kimi-K3 model card

Documented outputs

Gemini 4 Argon

text

Google Gemini 4 Argon model documentation

Kimi K3

text

Kimi-K3 model card

Provider availability

Gemini 4 Argon

Generally Available · Gemini API, Google AI Studio

Google Gemini 4 Argon model documentation

Kimi K3

Not sourced

Reasoning profile

Gemini 4 Argon

Reasoning

Kimi K3

Reasoning

Weight access

Gemini 4 Argon

Proprietary

Kimi K3

Open Weight

License

Gemini 4 Argon

Proprietary

Kimi K3

Open Weight

Release date

Gemini 4 Argon

2026-09-30

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: Gemini 4 Argon has the higher public score, 79.03 versus 68.42, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.13 vs $0.195. Cache-heavy agent loop: $0.16 vs $0.27.
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, Gemini 4 Argon or Kimi K3?

Gemini 4 Argon has the higher public score, 79.03 versus 68.42, 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, Gemini 4 Argon or Kimi K3?

Gemini 4 Argon has the higher public coding point estimate, 71.8 to 56.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 4 Argon or Kimi K3?

Gemini 4 Argon has the higher public agentic tasks point estimate, 74.9 to 65.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 4 Argon or Kimi K3?

For the stated presets, chat costs $0.007 on Gemini 4 Argon 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, Gemini 4 Argon or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 1M.

Self-host vs API cost

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

Gemini 4 Argon

API / mo$9,000

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Kimi K3

API / mo$13,500

Self-host / mo$57,606

Break-evenNot reached at 70% load

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 evidence85 rows

Agentic

AutomationBench
Gemini 4 Argon51.3%
Source
Kimi K330.8%
Source
Gemini 4 Argon leads this result
Finance Agent v2
Gemini 4 Argon65.4%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon57.40%
Source
Kimi K3—
Not directly comparable
Agents' Last Exam
Gemini 4 Argon39.5%
Source
Kimi K3—
Not directly comparable
OSWorld 2.0
Gemini 4 Argon69.2%
Source
Kimi K3—
Not directly comparable
CWE-bench v1
Gemini 4 Argon68.0%
Source
Kimi K3—
Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Gemini 4 Argon57.6%
Source
Kimi K3—
Not directly comparable
MazeBench
Gemini 4 Argon12%
Source
Kimi K31%
Source
Gemini 4 Argon leads this result
OSWorld 2.1 Offline
Gemini 4 Argon74.7%
Source
Kimi K3—
Not directly comparable
ExploitGym
Gemini 4 Argon39.5%
Source
Kimi K3—
Not directly comparable
RoboDojo Sim
Gemini 4 Argon34.2%
Source
Kimi K3—
Not directly comparable
CWE-bench
Gemini 4 Argon65.0%
Source
Kimi K3—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 4 Argon57.7%
Source
Kimi K380.9%
Source
Kimi K3 leads this result
Finch
Gemini 4 Argon—
Kimi K377.3%
Source
Not directly comparable
b3 Agent Security
Gemini 4 Argon—
Kimi K388.0%
Source
Not directly comparable
Cyber Misuse Refusal
Gemini 4 Argon—
Kimi K394.0%
Source
Not directly comparable
Terminal-Bench 2.1
Gemini 4 Argon—
Kimi K388.3%
Source
Not directly comparable
BrowseComp
Gemini 4 Argon—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
Gemini 4 Argon—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
Gemini 4 Argon—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
Gemini 4 Argon—
Kimi K384.2%
Source
Not directly comparable
JobBench
Gemini 4 Argon—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
Gemini 4 Argon—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
Gemini 4 Argon—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
Gemini 4 Argon—
Kimi K373.5%
Source
Not directly comparable
ApprenticeBench
Gemini 4 Argon—
Kimi K318%
Source
Not directly comparable
Hermes Index
Gemini 4 Argon—
Kimi K339.2%
Source
Not directly comparable
Hermes Bench
Gemini 4 Argon—
Kimi K371.5%
Source
Not directly comparable

Coding

ScarfBench
Shared source
Gemini 4 Argon75.2%
Kimi K361.3%
Gemini 4 Argon leads this result
DeepSWE
Gemini 4 Argon77.9%
Source
Kimi K367.5%
Source
Gemini 4 Argon leads this result
FrontierSWE v2
Shared source
Gemini 4 Argon55.0%
Kimi K325.9%
Gemini 4 Argon leads this result
Vibe Code Bench
Gemini 4 Argon91.90%
Source
Kimi K3—
Not directly comparable
PostTrainBench v1.1
Gemini 4 Argon45.3%
Source
Kimi K332.0%
Source
Gemini 4 Argon leads this result
Bug Hunt Bench
Shared source
Gemini 4 Argon40.7 fixes
Kimi K321.0 fixes
Gemini 4 Argon leads this result
FrontierCode 2.0
Shared source
Gemini 4 Argon26.3%
Kimi K317.7%
Gemini 4 Argon leads this result
CursorBench 4.0
Gemini 4 Argon52.9%
Source
Kimi K3—
Not directly comparable
PostTrainBench
Shared source
Gemini 4 Argon40.4%
Kimi K331.5%
Gemini 4 Argon leads this result
CursorBench 3.2
Gemini 4 Argon—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
Gemini 4 Argon—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
Gemini 4 Argon—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
Gemini 4 Argon—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
Gemini 4 Argon—
Kimi K342%
Source
Not directly comparable
PostTrainBench (lab runs)
Gemini 4 Argon—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
Gemini 4 Argon—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
Gemini 4 Argon—
Kimi K373.7%
Source
Not directly comparable
OpenHarmony Bench v1.0
Gemini 4 Argon—
Kimi K357.3%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemini 4 Argon—
Kimi K387.2%
Source
Not directly comparable
SWE-bench (Vals)
Gemini 4 Argon—
Kimi K393.4%
Source
Not directly comparable

Reasoning

STEMBench
Gemini 4 Argon2.44
Source
Kimi K3—
Not directly comparable
Graphwalks BFS 128K
Gemini 4 Argon99.7%
Source
Kimi K3—
Not directly comparable
GraphWalks BFS 256K–1M
Gemini 4 Argon84.2%
Source
Kimi K3—
Not directly comparable
ARC-AGI-1
Gemini 4 Argon98.50%
Source
Kimi K394.50%
Source
Gemini 4 Argon leads this result
ARC-AGI-2
Gemini 4 Argon91.3%
Source
Kimi K360.4%
Source
Gemini 4 Argon leads this result
ARC-AGI-3
Gemini 4 Argon46.3%
Source
Kimi K3—
Not directly comparable
ARC-AGI-3 Provider Adapter
Gemini 4 Argon97.30%
Source
Kimi K3—
Not directly comparable

Multimodal

ParseBench-2
Gemini 4 Argon84.20%
Source
Kimi K3—
Not directly comparable
ExtractBench Pro
Gemini 4 Argon30%
Source
Kimi K3—
Not directly comparable
Chartography (no tools)
Gemini 4 Argon71.6%
Source
Kimi K3—
Not directly comparable
LVBench
Gemini 4 Argon91.7%
Source
Kimi K3—
Not directly comparable
Blueprint-Bench 2
Gemini 4 Argon54.4%
Source
Kimi K3—
Not directly comparable
ExtractBench (LlamaIndex)
Gemini 4 Argon—
Kimi K383.17%
Source
Not directly comparable
OfficeQA Pro
Gemini 4 Argon—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
Gemini 4 Argon—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemini 4 Argon—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
Gemini 4 Argon—
Kimi K384.8%
Source
Not directly comparable
CharXiv
Gemini 4 Argon—
Kimi K391.3%
Source
Not directly comparable
MathVision
Gemini 4 Argon—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
Gemini 4 Argon—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
Gemini 4 Argon—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
Gemini 4 Argon—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
Gemini 4 Argon—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
Gemini 4 Argon—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
Gemini 4 Argon—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
Gemini 4 Argon—
Kimi K358.5%
Source
Not directly comparable

Knowledge

LABBench2
Gemini 4 Argon88.8%
Source
Kimi K3—
Not directly comparable
Morphology-to-molecule matching
Gemini 4 Argon25.2%
Source
Kimi K3—
Not directly comparable
GPQA
Gemini 4 Argon—
Kimi K393.5%
Source
Not directly comparable
GPQA-D
Gemini 4 Argon—
Kimi K393.5%
Source
Not directly comparable
HLE
Gemini 4 Argon—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
Gemini 4 Argon—
Kimi K343.5%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemini 4 Argon—
Kimi K392.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemini 4 Argon—
Kimi K388.0%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Shared source
Gemini 4 Argon0.7%
Kimi K352.7%
Gemini 4 Argon leads this result

Math

FrontierMath v2 (Tiers 1-3)
Gemini 4 Argon85.965%
Source
Kimi K3—
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 4 Argon78.049%
Source
Kimi K3—
Not directly comparable

85 public results · 13 shared

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Last updated November 13, 2026

Gemini 4 Argon vs Kimi K3

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

Or describe your own job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Your workload

Price against score, among all priced models

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, Gemini 4 Argon or Kimi K3?

Which is better for coding, Gemini 4 Argon or Kimi K3?

Which is better for agentic tasks, Gemini 4 Argon or Kimi K3?

Which costs less, Gemini 4 Argon or Kimi K3?

Which has the larger context window, Gemini 4 Argon or Kimi K3?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemini 4 Argon vs Kimi K3