Kimi K3 vs Ling 3.1 Flash

Updated November 13, 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, 68.42 versus 64.65. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 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

68.42/100

Supported · Public rank #17

90% interval 64.0–72.9

Model B

Ling 3.1 Flash

InclusionAI

64.65/100

Estimated · Public rank #30

Conditional range 50.3–79.0

API price per 1M tokens

Kimi K3: $3 input / $15 output

Ling 3.1 Flash: No comparable API price

Context window

Kimi K3: 1.05M

Ling 3.1 Flash: 262K

Released

Kimi K3: July 16, 2026

Ling 3.1 Flash: September 29, 2026

Shared results: 6
Kimi K3 only: 53
Ling 3.1 Flash only: 11
Like-for-like categories: 0 / 8

Supported: Kimi K3 · Estimated: Ling 3.1 Flash. 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

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 3.1 Flash 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
Ling 3.1 Flash is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.

56.2Kimi K356.8Ling 3.1 Flash

Directional only · BenchAlign v5.8

Ling 3.1 Flash 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.

AutomationBenchAgentic
Normalized gap 21.7
Kimi K3:30.8%
Ling 3.1 Flash:52.5%
Kimi K3 source Ling 3.1 Flash source
HLEKnowledge
Normalized gap 18.6
Kimi K3:56%
Ling 3.1 Flash:37.4%
Kimi K3 source Ling 3.1 Flash source
BrowseCompAgentic
Normalized gap 0.5
Kimi K3:91.2%
Ling 3.1 Flash:91.7%
Kimi K3 source Ling 3.1 Flash 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

Directional only

Kimi K3: 65.7; Supported · #16/135
Ling 3.1 Flash: 61.4; Estimated · #30/135
Basis: BenchAlign v5.8 lane · 18 vs 10 public rows
Reading: Directional only

Coding

Directional only

Kimi K3: 56.2; Supported · #24/162
Ling 3.1 Flash: 56.8; Estimated · #29/162
Basis: BenchAlign v5.8 lane · 18 vs 5 public rows
Reading: Directional only

Reasoning

Directional only

Kimi K3: 65.5; Supported · #21/160
Ling 3.1 Flash: 61.4; Estimated · #28/160
Basis: BenchAlign v5.8 lane · 2 vs 0 public rows
Reading: Directional only

Multimodal

Not comparable

Kimi K3: 67.7; Estimated · #19/67
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 14 vs 0 public rows
Reading: Not comparable

Knowledge

Not comparable

Kimi K3: 67.7; Supported · #20/180
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

Kimi K3: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Kimi K3: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Kimi K3: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Kimi K3 and Ling 3.1 Flash
Category	Kimi K3	Ling 3.1 Flash	Basis	Reading
Agentic	65.7Supported · #16/135	61.4Estimated · #30/135	Directional onlyBenchAlign v5.8 lane · 18 vs 10 public rows	Directional only
Coding	56.2Supported · #24/162	56.8Estimated · #29/162	Directional onlyBenchAlign v5.8 lane · 18 vs 5 public rows	Directional only
Reasoning	65.5Supported · #21/160	61.4Estimated · #28/160	Directional onlyBenchAlign v5.8 lane · 2 vs 0 public rows	Directional only
Multimodal	67.7Estimated · #19/67	Not ranked	Not comparableBenchAlign v5.8 lane · 14 vs 0 public rows	Not comparable
Knowledge	67.7Supported · #20/180	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 2 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, 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

Kimi K3: $0.0105; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request

Ling 3.1 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K3: $0.195; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request

Ling 3.1 Flash has no comparable published API token rate.

Cache-heavy agent loop

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

Kimi K3: $0.27; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request; Cached-input rate unavailable

Ling 3.1 Flash has no comparable published API token rate.

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.

Kimi K3

3 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).
Ling 3.1 Flash has no public API price, so it is not plotted.

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 Kimi K3 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.

SpecificationKimi K3Ling 3.1 Flash

Context window

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

Kimi K3

1.05M

Ling 3.1 Flash

262K

InclusionAI Ling 3.1 Flash released configuration

API model ID

Kimi K3

Not sourced

Ling 3.1 Flash

inclusionai/ling-3.1-flash

Vercel AI Gateway Ling 3.1 Flash route

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

Ling 3.1 Flash

No comparable hosted API rate

Vercel AI Gateway availability

Documented inputs

Kimi K3

text, image, video

Kimi-K3 model card

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Documented outputs

Kimi K3

text

Kimi-K3 model card

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Provider availability

Kimi K3

Not sourced

Ling 3.1 Flash

Generally Available · Hugging Face weights, SGLang, vLLM, Vercel AI Gateway API via Novita

Vercel AI Gateway Ling 3.1 Flash route

Reasoning profile

Kimi K3

Reasoning

Ling 3.1 Flash

Reasoning

Weight access

Kimi K3

Open Weight

Ling 3.1 Flash

Open Weight

License

Kimi K3

Open Weight

Ling 3.1 Flash

Open Weight

Release date

Kimi K3

2026-07-16

Ling 3.1 Flash

2026-09-29

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, 68.42 versus 64.65. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
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 Ling 3.1 Flash?

Kimi K3 has the higher public point estimate, 68.42 versus 64.65. Their conditional score ranges 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 Ling 3.1 Flash?

Ling 3.1 Flash scores higher for coding on the public lane, 56.8 to 56.2. Ling 3.1 Flash 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 Ling 3.1 Flash?

Kimi K3 scores higher for agentic tasks on the public lane, 65.7 to 61.4. Ling 3.1 Flash is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Kimi K3 or Ling 3.1 Flash?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Kimi K3 or Ling 3.1 Flash?

Kimi K3 has the larger documented context window: 1.05M, compared with 262K.

Self-host vs API cost

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

Kimi K3

API / mo$13,500

Self-host / mo$57,606

Break-evenNot reached at 70% load

Ling 3.1 Flash

API / mo (Novita AI)$900

Self-host / mo$27,962

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 evidence70 rows

Agentic

Finch
Kimi K377.3%
Source
Ling 3.1 Flash—
Not directly comparable
b3 Agent Security
Kimi K388.0%
Source
Ling 3.1 Flash—
Not directly comparable
Cyber Misuse Refusal
Kimi K394.0%
Source
Ling 3.1 Flash—
Not directly comparable
Terminal-Bench 2.1
Kimi K388.3%
Source
Ling 3.1 Flash81.2%
Source
Kimi K3 leads this result
BrowseComp
Kimi K391.2%
Source
Ling 3.1 Flash91.7%
Source
Ling 3.1 Flash leads this result
DeepSearchQA
Kimi K395.0%
Source
Ling 3.1 Flash—
Not directly comparable
Toolathlon-Verified
Kimi K373.2%
Source
Ling 3.1 Flash—
Not directly comparable
MCP Atlas
Kimi K384.2%
Source
Ling 3.1 Flash83%
Source
Kimi K3 leads this result
AutomationBench
Kimi K330.8%
Source
Ling 3.1 Flash52.5%
Source
Ling 3.1 Flash leads this result
JobBench
Kimi K352.9%
Source
Ling 3.1 Flash—
Not directly comparable
APEX-Agents
Kimi K337.6%
Source
Ling 3.1 Flash—
Not directly comparable
SpreadsheetBench 2
Kimi K334.8%
Source
Ling 3.1 Flash—
Not directly comparable
DECK-Bench
Kimi K373.5%
Source
Ling 3.1 Flash—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K380.9%
Source
Ling 3.1 Flash—
Not directly comparable
ApprenticeBench
Kimi K318%
Source
Ling 3.1 Flash—
Not directly comparable
Hermes Index
Kimi K339.2%
Source
Ling 3.1 Flash—
Not directly comparable
Hermes Bench
Kimi K371.5%
Source
Ling 3.1 Flash—
Not directly comparable
MazeBench
Kimi K31%
Source
Ling 3.1 Flash—
Not directly comparable
skillsBench
Kimi K3—
Ling 3.1 Flash68.7%
Source
Not directly comparable
CyberGym
Kimi K3—
Ling 3.1 Flash87.9%
Source
Not directly comparable
Finance Agent v2
Kimi K3—
Ling 3.1 Flash57.9%
Source
Not directly comparable
DRACO
Kimi K3—
Ling 3.1 Flash85.5%
Source
Not directly comparable
τ³-bench results
Kimi K3—
Ling 3.1 Flash47.8%
Source
Not directly comparable
WideResearch
Kimi K3—
Ling 3.1 Flash83.3%
Source
Not directly comparable

Coding

ScarfBench
Kimi K361.3%
Source
Ling 3.1 Flash—
Not directly comparable
DeepSWE
Kimi K367.5%
Source
Ling 3.1 Flash59.7%
Source
Kimi K3 leads this result
CursorBench 3.2
Kimi K360.8%
Source
Ling 3.1 Flash—
Not directly comparable
FrontierSWE
Kimi K381.2%
Source
Ling 3.1 Flash—
Not directly comparable
ProgramBench
Kimi K377.8%
Source
Ling 3.1 Flash—
Not directly comparable
Kimi Code Bench v2
Kimi K372.9%
Source
Ling 3.1 Flash—
Not directly comparable
sweMarathon
Kimi K342%
Source
Ling 3.1 Flash—
Not directly comparable
PostTrainBench (lab runs)
Kimi K336.6%
Source
Ling 3.1 Flash—
Not directly comparable
MLS-Bench Lite
Kimi K348.3%
Source
Ling 3.1 Flash—
Not directly comparable
VulcanBench v3
Kimi K373.7%
Source
Ling 3.1 Flash—
Not directly comparable
OpenHarmony Bench v1.0
Kimi K357.3%
Source
Ling 3.1 Flash—
Not directly comparable
FrontierSWE v2
Kimi K325.9%
Source
Ling 3.1 Flash—
Not directly comparable
LiveCodeBench (Vals)
Kimi K387.2%
Source
Ling 3.1 Flash—
Not directly comparable
SWE-bench (Vals)
Kimi K393.4%
Source
Ling 3.1 Flash—
Not directly comparable
PostTrainBench v1.1
Kimi K332.0%
Source
Ling 3.1 Flash—
Not directly comparable
Bug Hunt Bench
Kimi K321.0 fixes
Source
Ling 3.1 Flash—
Not directly comparable
FrontierCode 2.0
Kimi K317.7%
Source
Ling 3.1 Flash—
Not directly comparable
PostTrainBench
Kimi K331.5%
Source
Ling 3.1 Flash—
Not directly comparable
terminalBench4
Kimi K3—
Ling 3.1 Flash46.0%
Source
Not directly comparable
SWE-Atlas Codebase QnA
Kimi K3—
Ling 3.1 Flash64.5%
Source
Not directly comparable
SWE Multilingual
Kimi K3—
Ling 3.1 Flash88.5%
Source
Not directly comparable
SWE-bench Pro
Kimi K3—
Ling 3.1 Flash68.7%
Source
Not directly comparable

Reasoning

ARC-AGI-1
Kimi K394.50%
Source
Ling 3.1 Flash—
Not directly comparable
ARC-AGI-2
Kimi K360.4%
Source
Ling 3.1 Flash—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Kimi K383.17%
Source
Ling 3.1 Flash—
Not directly comparable
OfficeQA Pro
Kimi K363.3%
Source
Ling 3.1 Flash—
Not directly comparable
MMMU-Pro
Kimi K381.6%
Source
Ling 3.1 Flash—
Not directly comparable
MMMU-Pro w/ Python
Kimi K383.4%
Source
Ling 3.1 Flash—
Not directly comparable
CharXiv w/o tools
Kimi K384.8%
Source
Ling 3.1 Flash—
Not directly comparable
CharXiv
Kimi K391.3%
Source
Ling 3.1 Flash—
Not directly comparable
MathVision
Kimi K394.3%
Source
Ling 3.1 Flash—
Not directly comparable
MathVision w/ Python
Kimi K397.8%
Source
Ling 3.1 Flash—
Not directly comparable
BabyVision w/ Python
Kimi K385.7%
Source
Ling 3.1 Flash—
Not directly comparable
ZeroBench
Kimi K323.0%
Source
Ling 3.1 Flash—
Not directly comparable
ZeroBench w/ Python
Kimi K341.0%
Source
Ling 3.1 Flash—
Not directly comparable
WorldVQA ForceAnswer
Kimi K351.0%
Source
Ling 3.1 Flash—
Not directly comparable
OmniDocBench
Kimi K391.1%
Source
Ling 3.1 Flash—
Not directly comparable
PerceptionBench
Kimi K358.5%
Source
Ling 3.1 Flash—
Not directly comparable

Knowledge

GPQA
Kimi K393.5%
Source
Ling 3.1 Flash—
Not directly comparable
GPQA-D
Kimi K393.5%
Source
Ling 3.1 Flash—
Not directly comparable
HLE
Kimi K356%
Source
Ling 3.1 Flash37.4%
Source
Kimi K3 leads this result
HLE w/o tools
Kimi K343.5%
Source
Ling 3.1 Flash—
Not directly comparable
GPQA Diamond (Vals)
Kimi K392.9%
Source
Ling 3.1 Flash—
Not directly comparable
MMLU-Pro (Vals)
Kimi K388.0%
Source
Ling 3.1 Flash—
Not directly comparable
HealthBench Professional
Kimi K3—
Ling 3.1 Flash65.3%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Kimi K352.7%
Source
Ling 3.1 Flash—
Not directly comparable

70 public results · 6 shared

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Last updated November 13, 2026

Kimi K3 vs Ling 3.1 Flash

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

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

Multimodal

Knowledge

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

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, Kimi K3 or Ling 3.1 Flash?

Which is better for coding, Kimi K3 or Ling 3.1 Flash?

Which is better for agentic tasks, Kimi K3 or Ling 3.1 Flash?

Which costs less, Kimi K3 or Ling 3.1 Flash?

Which has the larger context window, Kimi K3 or Ling 3.1 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch Kimi K3 vs Ling 3.1 Flash