Ling 3.1 Flash vs MiniMax M3.1 Flash

Updated October 19, 2026. Rank says Ling 3.1 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Ling 3.1 Flash has the higher public point estimate, 68.08 versus 67.05. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Ling 3.1 Flash

InclusionAI

68.08/100

Estimated · Public rank #25

Conditional range 53.7–82.4

Model B

MiniMax M3.1 Flash

MiniMax

67.05/100

Estimated · Public rank #28

Conditional range 52.7–81.4

Shared results: 3
Ling 3.1 Flash only: 14
MiniMax M3.1 Flash only: 9
Like-for-like categories: 1 / 8

Estimated: Ling 3.1 Flash and MiniMax M3.1 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
MiniMax M3.1 Flash
MiniMax M3.1 Flash 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 and MiniMax M3.1 Flash are 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.

57.2Ling 3.1 Flash61.3MiniMax M3.1 Flash

Directional only · BenchAlign v5.8

MiniMax M3.1 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

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 9.7
Ling 3.1 Flash:52.5%
MiniMax M3.1 Flash:62.2%
Ling 3.1 Flash source MiniMax M3.1 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, 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

Ling 3.1 Flash: 63.4; Supported · #43/186
MiniMax M3.1 Flash: 73.2; Supported · #14/186
Basis: BenchAlign v5.8 lane · 2 vs 1 public rows
Reading: MiniMax M3.1 Flash leads · intervals overlap

Agentic

Directional only

Ling 3.1 Flash: 62.7; Estimated · #23/130
MiniMax M3.1 Flash: 63.9; Estimated · #19/130
Basis: BenchAlign v5.8 lane · 10 vs 6 public rows
Reading: Directional only

Coding

Directional only

Ling 3.1 Flash: 57.2; Estimated · #27/151
MiniMax M3.1 Flash: 61.3; Supported · #17/151
Basis: BenchAlign v5.8 lane · 5 vs 3 public rows
Reading: Directional only

Reasoning

Not comparable

Ling 3.1 Flash: 82.9; Unranked · 2 rankable rows
MiniMax M3.1 Flash: 80.6; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Ling 3.1 Flash: Not ranked
MiniMax M3.1 Flash: 73.5; #30/55
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Ling 3.1 Flash: Not ranked
MiniMax M3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Ling 3.1 Flash: Not ranked
MiniMax M3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Ling 3.1 Flash: Not ranked
MiniMax M3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Ling 3.1 Flash and MiniMax M3.1 Flash
Category	Ling 3.1 Flash	MiniMax M3.1 Flash	Basis	Reading
Knowledge	63.4Supported · #43/186	73.2Supported · #14/186	Like-for-likeBenchAlign v5.8 lane · 2 vs 1 public rows	MiniMax M3.1 Flash leads · intervals overlap
Agentic	62.7Estimated · #23/130	63.9Estimated · #19/130	Directional onlyBenchAlign v5.8 lane · 10 vs 6 public rows	Directional only
Coding	57.2Estimated · #27/151	61.3Supported · #17/151	Directional onlyBenchAlign v5.8 lane · 5 vs 3 public rows	Directional only
Reasoning	82.9Unranked · 2 rankable rows	80.6Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	73.5#30/55	Not comparableProvisional lane · 0 vs 1 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 v5.8) 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

Ling 3.1 Flash: API rate not published; Fits in one request
MiniMax M3.1 Flash: $0.00045; Fits in one request

Ling 3.1 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ling 3.1 Flash: API rate not published; Fits in one request
MiniMax M3.1 Flash: $0.0093; 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

Ling 3.1 Flash: API rate not published; Fits in one request; Cached-input rate unavailable
MiniMax M3.1 Flash: $0.012; Fits in one request

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

Specification differences

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

SpecificationLing 3.1 FlashMiniMax M3.1 Flash

Context window

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

Ling 3.1 Flash

262K

InclusionAI Ling 3.1 Flash released configuration

MiniMax M3.1 Flash

MiniMax: M3.1 Flash launch post

API model ID

Ling 3.1 Flash

inclusionai/ling-3.1-flash

Vercel AI Gateway Ling 3.1 Flash route

MiniMax M3.1 Flash

MiniMax-M3.1-Flash

MiniMax: M3.1 Flash launch post

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Ling 3.1 Flash

No comparable hosted API rate

Vercel AI Gateway availability

MiniMax M3.1 Flash

$0.015 per 1M cached input tokens

MiniMax: M3.1 Flash launch post

Documented inputs

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

MiniMax M3.1 Flash

Not sourced

Documented outputs

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

MiniMax M3.1 Flash

Not sourced

Provider availability

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

MiniMax M3.1 Flash

Not sourced

Reasoning profile

Ling 3.1 Flash

Reasoning

MiniMax M3.1 Flash

Reasoning

Weight access

Ling 3.1 Flash

Open Weight

MiniMax M3.1 Flash

Proprietary

License

Ling 3.1 Flash

Open Weight

MiniMax M3.1 Flash

Proprietary

Release date

Ling 3.1 Flash

2026-09-29

MiniMax M3.1 Flash

2026-10-19

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: Ling 3.1 Flash has the higher public point estimate, 68.08 versus 67.05. 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: MiniMax M3.1 Flash has the larger documented window (1M).

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

Questions

Which is better, Ling 3.1 Flash or MiniMax M3.1 Flash?

Ling 3.1 Flash has the higher public point estimate, 68.08 versus 67.05. 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, Ling 3.1 Flash or MiniMax M3.1 Flash?

MiniMax M3.1 Flash scores higher for coding on the public lane, 61.3 to 57.2. 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, Ling 3.1 Flash or MiniMax M3.1 Flash?

MiniMax M3.1 Flash scores higher for agentic tasks on the public lane, 63.9 to 62.7. Ling 3.1 Flash and MiniMax M3.1 Flash are 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, Ling 3.1 Flash or MiniMax M3.1 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, Ling 3.1 Flash or MiniMax M3.1 Flash?

MiniMax M3.1 Flash has the larger documented context window: 1M, compared with 262K.

Self-host vs API cost

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

Ling 3.1 Flash

API / mo$0

Self-host / mo$27,962

Break-even—

MiniMax M3.1 Flash

API / mo$563

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

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 evidence26 rows

Agentic

skillsBench
Ling 3.1 Flash68.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
AutomationBench
Ling 3.1 Flash52.5%
Source
MiniMax M3.1 Flash62.2%
Source
MiniMax M3.1 Flash leads this result
CyberGym
Ling 3.1 Flash87.9%
Source
MiniMax M3.1 Flash82.2%
Source
Ling 3.1 Flash leads this result
Finance Agent v2
Ling 3.1 Flash57.9%
Source
MiniMax M3.1 Flash—
Not directly comparable
DRACO
Ling 3.1 Flash85.5%
Source
MiniMax M3.1 Flash—
Not directly comparable
Terminal-Bench 2.1
Ling 3.1 Flash81.2%
Source
MiniMax M3.1 Flash—
Not directly comparable
τ³-bench results
Ling 3.1 Flash47.8%
Source
MiniMax M3.1 Flash—
Not directly comparable
MCP Atlas
Ling 3.1 Flash83%
Source
MiniMax M3.1 Flash—
Not directly comparable
BrowseComp
Ling 3.1 Flash91.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
WideResearch
Ling 3.1 Flash83.3%
Source
MiniMax M3.1 Flash—
Not directly comparable
BankerToolBench
Ling 3.1 Flash—
MiniMax M3.1 Flash76.2%
Source
Not directly comparable
Terminal-Bench 4.0
Ling 3.1 Flash—
MiniMax M3.1 Flash38.10%
Source
Not directly comparable
Agents' Last Exam
Ling 3.1 Flash—
MiniMax M3.1 Flash29.6%
Source
Not directly comparable
OSWorld 2.0
Ling 3.1 Flash—
MiniMax M3.1 Flash61.7%
Source
Not directly comparable

Coding

terminalBench4
Ling 3.1 Flash46.0%
Source
MiniMax M3.1 Flash—
Not directly comparable
SWE-Atlas Codebase QnA
Ling 3.1 Flash64.5%
Source
MiniMax M3.1 Flash—
Not directly comparable
SWE Multilingual
Ling 3.1 Flash88.5%
Source
MiniMax M3.1 Flash—
Not directly comparable
SWE-bench Pro
Ling 3.1 Flash68.7%
Source
MiniMax M3.1 Flash—
Not directly comparable
DeepSWE
Ling 3.1 Flash59.7%
Source
MiniMax M3.1 Flash73.7%
Source
MiniMax M3.1 Flash leads this result
PostTrainBench v1.1
Ling 3.1 Flash—
MiniMax M3.1 Flash39.3%
Source
Not directly comparable
NL2Repo
Ling 3.1 Flash—
MiniMax M3.1 Flash61.2%
Source
Not directly comparable

Multimodal

BabyVision w/ Python
Ling 3.1 Flash—
MiniMax M3.1 Flash91.0%
Source
Not directly comparable
CharXiv
Ling 3.1 Flash—
MiniMax M3.1 Flash82.5%
Source
Not directly comparable

Knowledge

HealthBench Professional
Ling 3.1 Flash65.3%
Source
MiniMax M3.1 Flash—
Not directly comparable
HLE
Ling 3.1 Flash37.4%
Source
MiniMax M3.1 Flash—
Not directly comparable
HLE w/o tools
Ling 3.1 Flash—
MiniMax M3.1 Flash43.4%
Source
Not directly comparable

26 public results · 3 shared

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Last updated October 19, 2026

Ling 3.1 Flash vs MiniMax M3.1 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

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Agentic

Coding

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, Ling 3.1 Flash or MiniMax M3.1 Flash?

Which is better for coding, Ling 3.1 Flash or MiniMax M3.1 Flash?

Which is better for agentic tasks, Ling 3.1 Flash or MiniMax M3.1 Flash?

Which costs less, Ling 3.1 Flash or MiniMax M3.1 Flash?

Which has the larger context window, Ling 3.1 Flash or MiniMax M3.1 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Related comparisons

Watch Ling 3.1 Flash vs MiniMax M3.1 Flash