Gemma 4 31B vs Ling 3.0 Flash

Updated October 20, 2026. Rank says Ling 3.0 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Ling 3.0 Flash has the higher public point estimate, 45.07 versus 39.84. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemma 4 31B

Google

39.84/100

Estimated · Public rank #139

Conditional range 30.1–49.6

Model B

Ling 3.0 Flash

InclusionAI

45.07/100

Estimated · Public rank #121

Conditional range 30.7–59.4

Shared results: 2
Gemma 4 31B only: 9
Ling 3.0 Flash only: 22
Like-for-like categories: 2 / 8

Estimated: Gemma 4 31B and Ling 3.0 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 3.0 Flash
Ling 3.0 Flash has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
No clear pick
The like-for-like coding result is a practical tie on the public lane (within 0.5 points).
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 31B 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.

31.7Gemma 4 31B31.6Ling 3.0 Flash

Like-for-like · BenchAlign v5.8

Gemma 4 31B 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.

HLEKnowledge
Normalized gap 3.8
Gemma 4 31B:26.5%
Ling 3.0 Flash:22.7%
Gemma 4 31B source Ling 3.0 Flash source
GPQAKnowledge
Normalized gap 0.7
Gemma 4 31B:84.3%
Ling 3.0 Flash:85.0%
Gemma 4 31B source Ling 3.0 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.

Coding

Like-for-like

Gemma 4 31B: 31.7; Supported · #90/152
Ling 3.0 Flash: 31.6; Supported · #92/152
Basis: BenchAlign v5.8 lane · 3 vs 6 public rows
Reading: Practical tie

Knowledge

Like-for-like

Gemma 4 31B: 40.9; Supported · #117/187
Ling 3.0 Flash: 43.5; Supported · #107/187
Basis: BenchAlign v5.8 lane · 4 vs 5 public rows
Reading: Ling 3.0 Flash leads · intervals overlap

Agentic

Directional only

Gemma 4 31B: 20.1; Estimated · #111/131
Ling 3.0 Flash: 33.4; Supported · #87/131
Basis: BenchAlign v5.8 lane · 1 vs 9 public rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 31B: 92.9; #15/149
Ling 3.0 Flash: 75.2; #68/149
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Gemma 4 31B: 65.8; Unranked · 3 rankable rows
Ling 3.0 Flash: 75.1; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 31B: 74.3; #26/55
Ling 3.0 Flash: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 31B: Not ranked
Ling 3.0 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 31B: Not ranked
Ling 3.0 Flash: 81.1; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 31B and Ling 3.0 Flash
Category	Gemma 4 31B	Ling 3.0 Flash	Basis	Reading
Coding	31.7Supported · #90/152	31.6Supported · #92/152	Like-for-likeBenchAlign v5.8 lane · 3 vs 6 public rows	Practical tie
Knowledge	40.9Supported · #117/187	43.5Supported · #107/187	Like-for-likeBenchAlign v5.8 lane · 4 vs 5 public rows	Ling 3.0 Flash leads · intervals overlap
Agentic	20.1Estimated · #111/131	33.4Supported · #87/131	Directional onlyBenchAlign v5.8 lane · 1 vs 9 public rows	Directional only
Instruction following	92.9#15/149	75.2#68/149	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	65.8Unranked · 3 rankable rows	75.1Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	74.3#26/55	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	81.1Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 2 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

Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request
Ling 3.0 Flash: API rate not published; Fits in one request

Gemma 4 31B has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request
Ling 3.0 Flash: API rate not published; Fits in one request

Gemma 4 31B has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 31B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Ling 3.0 Flash: API rate not published; Fits in one request; Cached-input rate unavailable

Gemma 4 31B has no comparable published API token rate. Ling 3.0 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.

SpecificationGemma 4 31BLing 3.0 Flash

Context window

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

Gemma 4 31B

256K

Google Gemma 4 model documentation

Ling 3.0 Flash

262K

InclusionAI Ling 3.0 Flash model card

API model ID

Gemma 4 31B

gemma-4-31b-it

Google Gemma Gemini API guide

Ling 3.0 Flash

Not sourced

Cached-input rate

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

Gemma 4 31B

No comparable hosted API rate

Ling 3.0 Flash

No comparable hosted API rate

InclusionAI Ling 3.0 Flash model card

Documented inputs

Gemma 4 31B

text, image

Google Gemma 4 model documentation

Ling 3.0 Flash

Not sourced

Documented outputs

Gemma 4 31B

text

Google Gemma 4 model documentation

Ling 3.0 Flash

Not sourced

Provider availability

Gemma 4 31B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Ling 3.0 Flash

Not sourced

Reasoning profile

Gemma 4 31B

Reasoning

Ling 3.0 Flash

Reasoning

Weight access

Gemma 4 31B

Open Weight

Ling 3.0 Flash

Open Weight

License

Gemma 4 31B

Open Weight

Ling 3.0 Flash

Open Weight

Release date

Gemma 4 31B

2026-04-02

Ling 3.0 Flash

2026-07-23

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 3.0 Flash has the higher public point estimate, 45.07 versus 39.84. 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: Ling 3.0 Flash has the larger documented window (262K).

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

Questions

Which is better, Gemma 4 31B or Ling 3.0 Flash?

Ling 3.0 Flash has the higher public point estimate, 45.07 versus 39.84. 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, Gemma 4 31B or Ling 3.0 Flash?

The like-for-like coding row is a practical tie on the public lane, 31.7 against 31.6, inside the 0.5-point band BenchLM treats as level.

Which is better for agentic tasks, Gemma 4 31B or Ling 3.0 Flash?

Ling 3.0 Flash scores higher for agentic tasks on the public lane, 33.4 to 20.1. Gemma 4 31B 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, Gemma 4 31B or Ling 3.0 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, Gemma 4 31B or Ling 3.0 Flash?

Ling 3.0 Flash has the larger documented context window: 262K, compared with 256K.

Self-host vs API cost

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

Gemma 4 31B

API / mo$0

Self-host / mo$298

Break-even—

Ling 3.0 Flash

API / mo$0

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 evidence33 rows

Agentic

Gert Labs
Gemma 4 31B35.26%
Source
Ling 3.0 Flash—
Not directly comparable
MCP Atlas
Gemma 4 31B—
Ling 3.0 Flash65.5%
Source
Not directly comparable
skillsBench
Gemma 4 31B—
Ling 3.0 Flash44.8%
Source
Not directly comparable
BFCL v4
Gemma 4 31B—
Ling 3.0 Flash73.0%
Source
Not directly comparable
WideResearch
Gemma 4 31B—
Ling 3.0 Flash73.6%
Source
Not directly comparable
BrowseComp
Gemma 4 31B—
Ling 3.0 Flash72.2%
Source
Not directly comparable
DRACO
Gemma 4 31B—
Ling 3.0 Flash70.4%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 31B—
Ling 3.0 Flash50.2%
Source
Not directly comparable
Hermes Index
Gemma 4 31B—
Ling 3.0 Flash21.6%
Source
Not directly comparable
Hermes Bench
Gemma 4 31B—
Ling 3.0 Flash53.8%
Source
Not directly comparable

Coding

LiveCodeBench v6
Gemma 4 31B80.0%
Source
Ling 3.0 Flash—
Not directly comparable
SWE-Rebench
Gemma 4 31B41.6%
Source
Ling 3.0 Flash—
Not directly comparable
React Native Evals
Gemma 4 31B75.2%
Source
Ling 3.0 Flash—
Not directly comparable
SWE-bench Pro
Gemma 4 31B—
Ling 3.0 Flash56.6%
Source
Not directly comparable
SWE Multilingual
Gemma 4 31B—
Ling 3.0 Flash72.4%
Source
Not directly comparable
LiveCodeBench v5
Gemma 4 31B—
Ling 3.0 Flash82.8%
Source
Not directly comparable
SciCode
Gemma 4 31B—
Ling 3.0 Flash41.2%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 31B—
Ling 3.0 Flash84.0%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 31B—
Ling 3.0 Flash65.2%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 31B66.4%
Source
Ling 3.0 Flash—
Not directly comparable

Multimodal

ParseBench
Gemma 4 31B62.40%
Source
Ling 3.0 Flash—
Not directly comparable
MMMU-Pro
Gemma 4 31B76.9%
Source
Ling 3.0 Flash—
Not directly comparable

Knowledge

GPQA
Gemma 4 31B84.3%
Source
Ling 3.0 Flash85.0%
Source
Ling 3.0 Flash leads this result
MMLU-Pro
Gemma 4 31B85.2%
Source
Ling 3.0 Flash—
Not directly comparable
HLE
Gemma 4 31B26.5%
Source
Ling 3.0 Flash22.7%
Source
Gemma 4 31B leads this result
HLE w/o tools
Gemma 4 31B19.5%
Source
Ling 3.0 Flash—
Not directly comparable
GPQA-D
Gemma 4 31B—
Ling 3.0 Flash85.0%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 31B—
Ling 3.0 Flash84.8%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 31B—
Ling 3.0 Flash82.0%
Source
Not directly comparable

Instruction following

IFBench
Gemma 4 31B—
Ling 3.0 Flash74.5%
Source
Not directly comparable

Math

AIME26
Gemma 4 31B—
Ling 3.0 Flash93.2%
Source
Not directly comparable
HMMT Feb 2026
Gemma 4 31B—
Ling 3.0 Flash87.0%
Source
Not directly comparable
IMOAnswerBench
Gemma 4 31B—
Ling 3.0 Flash83.7%
Source
Not directly comparable

33 public results · 2 shared

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Last updated October 20, 2026

Gemma 4 31B vs Ling 3.0 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

Coding

Knowledge

Agentic

Instruction following

Reasoning

Multimodal

Multilingual

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, Gemma 4 31B or Ling 3.0 Flash?

Which is better for coding, Gemma 4 31B or Ling 3.0 Flash?

Which is better for agentic tasks, Gemma 4 31B or Ling 3.0 Flash?

Which costs less, Gemma 4 31B or Ling 3.0 Flash?

Which has the larger context window, Gemma 4 31B or Ling 3.0 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemma 4 31B vs Ling 3.0 Flash