GPT-OSS 20B 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 36.86. 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

GPT-OSS 20B

OpenAI

36.86/100

Estimated · Public rank #149

Conditional range 27.1–46.6

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

GPT-OSS 20B: No comparable API price

Ling 3.1 Flash: No comparable API price

Context window

GPT-OSS 20B: 128K

Ling 3.1 Flash: 262K

Released

GPT-OSS 20B: August 5, 2025

Ling 3.1 Flash: September 29, 2026

Shared results: 1
GPT-OSS 20B only: 6
Ling 3.1 Flash only: 16
Like-for-like categories: 0 / 8

Estimated: GPT-OSS 20B 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
GPT-OSS 20B and Ling 3.1 Flash are 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
GPT-OSS 20B and Ling 3.1 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-OSS 20B does not fit this workload in one request. GPT-OSS 20B 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.

23.8GPT-OSS 20B56.8Ling 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.

HLEKnowledge
Normalized gap 26.5
GPT-OSS 20B:10.9%
Ling 3.1 Flash:37.4%
GPT-OSS 20B 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

GPT-OSS 20B: 22.9; Estimated · #116/135
Ling 3.1 Flash: 61.4; Estimated · #30/135
Basis: BenchAlign v5.8 lane · 0 vs 10 public rows
Reading: Directional only

Coding

Directional only

GPT-OSS 20B: 23.8; Estimated · #130/162
Ling 3.1 Flash: 56.8; Estimated · #29/162
Basis: BenchAlign v5.8 lane · 2 vs 5 public rows
Reading: Directional only

Reasoning

Directional only

GPT-OSS 20B: 30.6; Estimated · #120/160
Ling 3.1 Flash: 61.4; Estimated · #28/160
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Directional only

Multimodal

Not comparable

GPT-OSS 20B: Not ranked
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Not comparable

Knowledge

Not comparable

GPT-OSS 20B: 35.6; Supported · #124/180
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 3 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

GPT-OSS 20B: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-OSS 20B: 79.2; #64/157
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-OSS 20B: 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 GPT-OSS 20B and Ling 3.1 Flash
Category	GPT-OSS 20B	Ling 3.1 Flash	Basis	Reading
Agentic	22.9Estimated · #116/135	61.4Estimated · #30/135	Directional onlyBenchAlign v5.8 lane · 0 vs 10 public rows	Directional only
Coding	23.8Estimated · #130/162	56.8Estimated · #29/162	Directional onlyBenchAlign v5.8 lane · 2 vs 5 public rows	Directional only
Reasoning	30.6Estimated · #120/160	61.4Estimated · #28/160	Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows	Directional only
Multimodal	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows	Not comparable
Knowledge	35.6Supported · #124/180	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	79.2#64/157	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

GPT-OSS 20B: Self-hosted; infrastructure cost varies; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request

GPT-OSS 20B 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

GPT-OSS 20B: Self-hosted; infrastructure cost varies; Fits in one request
Ling 3.1 Flash: API rate not published; Fits in one request

GPT-OSS 20B 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

GPT-OSS 20B: 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

GPT-OSS 20B does not fit this workload in one request. GPT-OSS 20B 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.

SpecificationGPT-OSS 20BLing 3.1 Flash

Context window

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

GPT-OSS 20B

128K

Ling 3.1 Flash

262K

InclusionAI Ling 3.1 Flash released configuration

API model ID

GPT-OSS 20B

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.

GPT-OSS 20B

No comparable hosted API rate

Ling 3.1 Flash

No comparable hosted API rate

Vercel AI Gateway availability

Documented inputs

GPT-OSS 20B

text

OpenAI gpt-oss-20b model card

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Documented outputs

GPT-OSS 20B

text

OpenAI gpt-oss-20b model card

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Provider availability

GPT-OSS 20B

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

GPT-OSS 20B

Non-Reasoning

Ling 3.1 Flash

Reasoning

Weight access

GPT-OSS 20B

Open Weight

Ling 3.1 Flash

Open Weight

License

GPT-OSS 20B

Open Weight

Ling 3.1 Flash

Open Weight

Release date

GPT-OSS 20B

2025-08-05

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 36.86. 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, GPT-OSS 20B or Ling 3.1 Flash?

Ling 3.1 Flash has the higher public point estimate, 64.65 versus 36.86. 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, GPT-OSS 20B or Ling 3.1 Flash?

Ling 3.1 Flash scores higher for coding on the public lane, 56.8 to 23.8. GPT-OSS 20B and Ling 3.1 Flash are 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, GPT-OSS 20B or Ling 3.1 Flash?

Ling 3.1 Flash scores higher for agentic tasks on the public lane, 61.4 to 22.9. GPT-OSS 20B and Ling 3.1 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, GPT-OSS 20B 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, GPT-OSS 20B 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.

GPT-OSS 20B

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 evidence23 rows

Agentic

skillsBench
GPT-OSS 20B—
Ling 3.1 Flash68.7%
Source
Not directly comparable
AutomationBench
GPT-OSS 20B—
Ling 3.1 Flash52.5%
Source
Not directly comparable
CyberGym
GPT-OSS 20B—
Ling 3.1 Flash87.9%
Source
Not directly comparable
Finance Agent v2
GPT-OSS 20B—
Ling 3.1 Flash57.9%
Source
Not directly comparable
DRACO
GPT-OSS 20B—
Ling 3.1 Flash85.5%
Source
Not directly comparable
Terminal-Bench 2.1
GPT-OSS 20B—
Ling 3.1 Flash81.2%
Source
Not directly comparable
τ³-bench results
GPT-OSS 20B—
Ling 3.1 Flash47.8%
Source
Not directly comparable
MCP Atlas
GPT-OSS 20B—
Ling 3.1 Flash83%
Source
Not directly comparable
BrowseComp
GPT-OSS 20B—
Ling 3.1 Flash91.7%
Source
Not directly comparable
WideResearch
GPT-OSS 20B—
Ling 3.1 Flash83.3%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-OSS 20B60.7%
Source
Ling 3.1 Flash—
Not directly comparable
React Native Evals
GPT-OSS 20B71%
Source
Ling 3.1 Flash—
Not directly comparable
terminalBench4
GPT-OSS 20B—
Ling 3.1 Flash46.0%
Source
Not directly comparable
SWE-Atlas Codebase QnA
GPT-OSS 20B—
Ling 3.1 Flash64.5%
Source
Not directly comparable
SWE Multilingual
GPT-OSS 20B—
Ling 3.1 Flash88.5%
Source
Not directly comparable
SWE-bench Pro
GPT-OSS 20B—
Ling 3.1 Flash68.7%
Source
Not directly comparable
DeepSWE
GPT-OSS 20B—
Ling 3.1 Flash59.7%
Source
Not directly comparable

Knowledge

MMLU
GPT-OSS 20B85.3%
Source
Ling 3.1 Flash—
Not directly comparable
GPQA
GPT-OSS 20B71.5%
Source
Ling 3.1 Flash—
Not directly comparable
HLE
GPT-OSS 20B10.9%
Source
Ling 3.1 Flash37.4%
Source
Ling 3.1 Flash leads this result
HealthBench Professional
GPT-OSS 20B—
Ling 3.1 Flash65.3%
Source
Not directly comparable

Math

AIME 2024
GPT-OSS 20B92.1%
Source
Ling 3.1 Flash—
Not directly comparable
AIME 2025
GPT-OSS 20B91.7%
Source
Ling 3.1 Flash—
Not directly comparable

23 public results · 1 shared

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Last updated November 13, 2026

GPT-OSS 20B 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

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, GPT-OSS 20B or Ling 3.1 Flash?

Which is better for coding, GPT-OSS 20B or Ling 3.1 Flash?

Which is better for agentic tasks, GPT-OSS 20B or Ling 3.1 Flash?

Which costs less, GPT-OSS 20B or Ling 3.1 Flash?

Which has the larger context window, GPT-OSS 20B or Ling 3.1 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Related comparisons

Watch GPT-OSS 20B vs Ling 3.1 Flash