DeepSeek V3.1 (Reasoning) vs Ling 3.1 Flash

Updated November 13, 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, 64.65 versus 38.66. 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

DeepSeek V3.1 (Reasoning)

DeepSeek

38.66/100

Estimated · Public rank #145

Conditional range 28.9–48.4

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

DeepSeek V3.1 (Reasoning): No comparable API price

Ling 3.1 Flash: No comparable API price

Context window

DeepSeek V3.1 (Reasoning): 128K

Ling 3.1 Flash: 262K

Released

DeepSeek V3.1 (Reasoning): August 21, 2025

Ling 3.1 Flash: September 29, 2026

Shared results: 1
DeepSeek V3.1 (Reasoning) only: 5
Ling 3.1 Flash only: 16
Like-for-like categories: 0 / 8

Estimated: DeepSeek V3.1 (Reasoning) and 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
Ling 3.1 Flash
Ling 3.1 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
DeepSeek V3.1 (Reasoning) 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
DeepSeek V3.1 (Reasoning) is not ranked on the public lane for agentic, so no winner is named for agentic.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3.1 (Reasoning) does not fit this workload in one request. DeepSeek V3.1 (Reasoning) has no comparable published API token rate. Ling 3.1 Flash has no comparable published API token rate.
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.

—DeepSeek V3.1 (Reasoning)56.8Ling 3.1 Flash

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

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.

HLEKnowledge
Normalized gap 21.5
DeepSeek V3.1 (Reasoning):15.9%
Ling 3.1 Flash:37.4%
DeepSeek V3.1 (Reasoning) 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.

Reasoning

Directional only

DeepSeek V3.1 (Reasoning): 31.9; Estimated · #114/160
Ling 3.1 Flash: 61.4; Estimated · #28/160
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Directional only

Agentic

Not comparable

DeepSeek V3.1 (Reasoning): Not ranked
Ling 3.1 Flash: 61.4; Estimated · #30/135
Basis: BenchAlign v5.8 lane · 0 vs 10 public rows
Reading: Not comparable

Coding

Not comparable

DeepSeek V3.1 (Reasoning): Not ranked
Ling 3.1 Flash: 56.8; Estimated · #29/162
Basis: BenchAlign v5.8 lane · 1 vs 5 public rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3.1 (Reasoning): Not ranked
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Not comparable

Knowledge

Not comparable

DeepSeek V3.1 (Reasoning): 40.3; Supported · #112/180
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 3 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3.1 (Reasoning): Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

DeepSeek V3.1 (Reasoning): 48.4; #102/157
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3.1 (Reasoning): 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 DeepSeek V3.1 (Reasoning) and Ling 3.1 Flash
Category	DeepSeek V3.1 (Reasoning)	Ling 3.1 Flash	Basis	Reading
Reasoning	31.9Estimated · #114/160	61.4Estimated · #28/160	Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows	Directional only
Agentic	Not ranked	61.4Estimated · #30/135	Not comparableBenchAlign v5.8 lane · 0 vs 10 public rows	Not comparable
Coding	Not ranked	56.8Estimated · #29/162	Not comparableBenchAlign v5.8 lane · 1 vs 5 public rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows	Not comparable
Knowledge	40.3Supported · #112/180	Not ranked	Not comparableBenchAlign v5.8 lane · 3 vs 2 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	48.4#102/157	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

DeepSeek V3.1 (Reasoning): Self-hosted; infrastructure cost varies; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request

DeepSeek V3.1 (Reasoning) has no comparable published API token rate. Ling 3.1 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3.1 (Reasoning): Self-hosted; infrastructure cost varies; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request

DeepSeek V3.1 (Reasoning) has no comparable published API token rate. Ling 3.1 Flash has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V3.1 (Reasoning): Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Ling 3.1 Flash: API rate not published; Fits in one request; Cached-input rate unavailable

DeepSeek V3.1 (Reasoning) does not fit this workload in one request. DeepSeek V3.1 (Reasoning) has no comparable published API token rate. 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.

SpecificationDeepSeek V3.1 (Reasoning)Ling 3.1 Flash

Context window

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

DeepSeek V3.1 (Reasoning)

128K

Ling 3.1 Flash

262K

InclusionAI Ling 3.1 Flash released configuration

API model ID

DeepSeek V3.1 (Reasoning)

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.

DeepSeek V3.1 (Reasoning)

No comparable hosted API rate

Ling 3.1 Flash

No comparable hosted API rate

Vercel AI Gateway availability

Documented inputs

DeepSeek V3.1 (Reasoning)

text

DeepSeek-V3.1 model card

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Documented outputs

DeepSeek V3.1 (Reasoning)

text

DeepSeek-V3.1 model card

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Provider availability

DeepSeek V3.1 (Reasoning)

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

DeepSeek V3.1 (Reasoning)

Reasoning

Ling 3.1 Flash

Reasoning

Weight access

DeepSeek V3.1 (Reasoning)

Open Weight

Ling 3.1 Flash

Open Weight

License

DeepSeek V3.1 (Reasoning)

Open Weight

Ling 3.1 Flash

Open Weight

Release date

DeepSeek V3.1 (Reasoning)

2025-08-21

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: Ling 3.1 Flash has the higher public point estimate, 64.65 versus 38.66. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Ling 3.1 Flash has the larger documented window (262K).

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

Questions

Which is better, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Ling 3.1 Flash has the higher public point estimate, 64.65 versus 38.66. 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, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

DeepSeek V3.1 (Reasoning) is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

DeepSeek V3.1 (Reasoning) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V3.1 (Reasoning) 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, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Ling 3.1 Flash has the larger documented context window: 262K, compared with 128K.

Self-host vs API cost

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

DeepSeek V3.1 (Reasoning)

API / moNo API price on file

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

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 evidence22 rows

Agentic

skillsBench
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash68.7%
Source
Not directly comparable
AutomationBench
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash52.5%
Source
Not directly comparable
CyberGym
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash87.9%
Source
Not directly comparable
Finance Agent v2
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash57.9%
Source
Not directly comparable
DRACO
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash85.5%
Source
Not directly comparable
Terminal-Bench 2.1
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash81.2%
Source
Not directly comparable
τ³-bench results
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash47.8%
Source
Not directly comparable
MCP Atlas
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash83%
Source
Not directly comparable
BrowseComp
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash91.7%
Source
Not directly comparable
WideResearch
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash83.3%
Source
Not directly comparable

Coding

LiveCodeBench
DeepSeek V3.1 (Reasoning)74.8%
Source
Ling 3.1 Flash—
Not directly comparable
terminalBench4
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash46.0%
Source
Not directly comparable
SWE-Atlas Codebase QnA
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash64.5%
Source
Not directly comparable
SWE Multilingual
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash88.5%
Source
Not directly comparable
SWE-bench Pro
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash68.7%
Source
Not directly comparable
DeepSWE
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash59.7%
Source
Not directly comparable

Knowledge

GPQA
DeepSeek V3.1 (Reasoning)80.1%
Source
Ling 3.1 Flash—
Not directly comparable
MMLU-Pro
DeepSeek V3.1 (Reasoning)84.8%
Source
Ling 3.1 Flash—
Not directly comparable
HLE
DeepSeek V3.1 (Reasoning)15.9%
Source
Ling 3.1 Flash37.4%
Source
Ling 3.1 Flash leads this result
HealthBench Professional
DeepSeek V3.1 (Reasoning)—
Ling 3.1 Flash65.3%
Source
Not directly comparable

Math

AIME 2024
DeepSeek V3.1 (Reasoning)93.1%
Source
Ling 3.1 Flash—
Not directly comparable
AIME 2025
DeepSeek V3.1 (Reasoning)88.4%
Source
Ling 3.1 Flash—
Not directly comparable

22 public results · 1 shared

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Last updated November 13, 2026

DeepSeek V3.1 (Reasoning) 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

Reasoning

Agentic

Coding

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, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Which is better for coding, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Which is better for agentic tasks, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Which costs less, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Which has the larger context window, DeepSeek V3.1 (Reasoning) or Ling 3.1 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Related comparisons

Watch DeepSeek V3.1 (Reasoning) vs Ling 3.1 Flash