Gemini 4 Carbon vs Ling 3.1 Flash

Updated October 28, 2026. Rank says Gemini 4 Carbon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 4 Carbon has the higher public point estimate, 88.42 versus 65.47. Their conditional score ranges 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

Gemini 4 Carbon

Google

88.42/100

Estimated · Public rank #1

Conditional range 74.1–100.0

Model B

Ling 3.1 Flash

InclusionAI

65.47/100

Estimated · Public rank #27

Conditional range 51.1–79.8

Shared results: 1
Gemini 4 Carbon only: 8
Ling 3.1 Flash only: 16
Like-for-like categories: 0 / 8

Estimated: Gemini 4 Carbon 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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
Gemini 4 Carbon is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.

82.2Gemini 4 Carbon58.5Ling 3.1 Flash

Directional only · BenchAlign v5.8

Gemini 4 Carbon 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 1.4
Gemini 4 Carbon:53.9%
Ling 3.1 Flash:52.5%
Gemini 4 Carbon 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, 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

Directional only

Gemini 4 Carbon: 82.2; Supported · #3/156
Ling 3.1 Flash: 58.5; Estimated · #23/156
Basis: BenchAlign v5.8 lane · 1 vs 5 public rows
Reading: Directional only

Agentic

Not comparable

Gemini 4 Carbon: Not ranked
Ling 3.1 Flash: 63.4; Estimated · #17/134
Basis: BenchAlign v5.8 lane · 4 vs 10 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 4 Carbon: 79.5; Unranked · 2 rankable rows
Ling 3.1 Flash: 82.0; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 4 Carbon: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Gemini 4 Carbon: 88.2; Supported · #2/178
Ling 3.1 Flash: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 4 Carbon: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 4 Carbon: Not ranked
Ling 3.1 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 4 Carbon: 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 Gemini 4 Carbon and Ling 3.1 Flash
Category	Gemini 4 Carbon	Ling 3.1 Flash	Basis	Reading
Coding	82.2Supported · #3/156	58.5Estimated · #23/156	Directional onlyBenchAlign v5.8 lane · 1 vs 5 public rows	Directional only
Agentic	Not ranked	63.4Estimated · #17/134	Not comparableBenchAlign v5.8 lane · 4 vs 10 public rows	Not comparable
Reasoning	79.5Unranked · 2 rankable rows	82.0Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	88.2Supported · #2/178	Not ranked	Not comparableBenchAlign v5.8 lane · 0 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, 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

Gemini 4 Carbon: $0.007; Fit state unavailable
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

Gemini 4 Carbon: $0.13; Fit state unavailable
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

Gemini 4 Carbon: $0.16; Fit state unavailable
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

Specification differences

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

SpecificationGemini 4 CarbonLing 3.1 Flash

Context window

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

Gemini 4 Carbon

Not sourced

Google Gemini 4 Carbon launch

Ling 3.1 Flash

262K

InclusionAI Ling 3.1 Flash released configuration

API model ID

Gemini 4 Carbon

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.

Gemini 4 Carbon

$0.1 per 1M cached input tokens

Google Gemini 4 Carbon launch post

Ling 3.1 Flash

No comparable hosted API rate

Vercel AI Gateway availability

Documented inputs

Gemini 4 Carbon

Not sourced

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Documented outputs

Gemini 4 Carbon

Not sourced

Ling 3.1 Flash

text

InclusionAI Ling 3.1 Flash model card

Provider availability

Gemini 4 Carbon

Limited Access · Fairwind Program

Google Gemini 4 Carbon launch

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

Gemini 4 Carbon

Reasoning

Ling 3.1 Flash

Reasoning

Weight access

Gemini 4 Carbon

Proprietary

Ling 3.1 Flash

Open Weight

License

Gemini 4 Carbon

Proprietary

Ling 3.1 Flash

Open Weight

Release date

Gemini 4 Carbon

2026-10-28

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: Gemini 4 Carbon has the higher public point estimate, 88.42 versus 65.47. 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: A complete documented context comparison is not available.

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

Questions

Which is better, Gemini 4 Carbon or Ling 3.1 Flash?

Gemini 4 Carbon has the higher public point estimate, 88.42 versus 65.47. 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, Gemini 4 Carbon or Ling 3.1 Flash?

Gemini 4 Carbon scores higher for coding on the public lane, 82.2 to 58.5. 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, Gemini 4 Carbon or Ling 3.1 Flash?

Gemini 4 Carbon is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 4 Carbon 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, Gemini 4 Carbon or Ling 3.1 Flash?

A complete documented context-window comparison is not available.

Self-host vs API cost

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

Gemini 4 Carbon

API / mo$9,000

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Ling 3.1 Flash

API / mo$0

Self-host / mo$27,962

Break-even—

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 evidence25 rows

Agentic

AutomationBench
Gemini 4 Carbon53.9%
Source
Ling 3.1 Flash52.5%
Source
Gemini 4 Carbon leads this result
OSWorld 2.1 Offline
Gemini 4 Carbon81.5%
Source
Ling 3.1 Flash—
Not directly comparable
CWE-bench v1
Gemini 4 Carbon71.0%
Source
Ling 3.1 Flash—
Not directly comparable
ExploitGym
Gemini 4 Carbon53.5%
Source
Ling 3.1 Flash—
Not directly comparable
skillsBench
Gemini 4 Carbon—
Ling 3.1 Flash68.7%
Source
Not directly comparable
CyberGym
Gemini 4 Carbon—
Ling 3.1 Flash87.9%
Source
Not directly comparable
Finance Agent v2
Gemini 4 Carbon—
Ling 3.1 Flash57.9%
Source
Not directly comparable
DRACO
Gemini 4 Carbon—
Ling 3.1 Flash85.5%
Source
Not directly comparable
Terminal-Bench 2.1
Gemini 4 Carbon—
Ling 3.1 Flash81.2%
Source
Not directly comparable
τ³-bench results
Gemini 4 Carbon—
Ling 3.1 Flash47.8%
Source
Not directly comparable
MCP Atlas
Gemini 4 Carbon—
Ling 3.1 Flash83%
Source
Not directly comparable
BrowseComp
Gemini 4 Carbon—
Ling 3.1 Flash91.7%
Source
Not directly comparable
WideResearch
Gemini 4 Carbon—
Ling 3.1 Flash83.3%
Source
Not directly comparable

Coding

FrontierSWE v2
Gemini 4 Carbon61.2%
Source
Ling 3.1 Flash—
Not directly comparable
terminalBench4
Gemini 4 Carbon—
Ling 3.1 Flash46.0%
Source
Not directly comparable
SWE-Atlas Codebase QnA
Gemini 4 Carbon—
Ling 3.1 Flash64.5%
Source
Not directly comparable
SWE Multilingual
Gemini 4 Carbon—
Ling 3.1 Flash88.5%
Source
Not directly comparable
SWE-bench Pro
Gemini 4 Carbon—
Ling 3.1 Flash68.7%
Source
Not directly comparable
DeepSWE
Gemini 4 Carbon—
Ling 3.1 Flash59.7%
Source
Not directly comparable

Reasoning

GraphWalks BFS 256K–1M
Gemini 4 Carbon96.2%
Source
Ling 3.1 Flash—
Not directly comparable

Multimodal

Chartography (no tools)
Gemini 4 Carbon72.5%
Source
Ling 3.1 Flash—
Not directly comparable
Blueprint-Bench 2
Gemini 4 Carbon58.6%
Source
Ling 3.1 Flash—
Not directly comparable

Knowledge

HealthBench Professional
Gemini 4 Carbon—
Ling 3.1 Flash65.3%
Source
Not directly comparable
HLE
Gemini 4 Carbon—
Ling 3.1 Flash37.4%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Gemini 4 Carbon0.0%
Source
Ling 3.1 Flash—
Not directly comparable

25 public results · 1 shared

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Last updated October 28, 2026

Gemini 4 Carbon vs Ling 3.1 Flash

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

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

Agentic

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, Gemini 4 Carbon or Ling 3.1 Flash?

Which is better for coding, Gemini 4 Carbon or Ling 3.1 Flash?

Which is better for agentic tasks, Gemini 4 Carbon or Ling 3.1 Flash?

Which costs less, Gemini 4 Carbon or Ling 3.1 Flash?

Which has the larger context window, Gemini 4 Carbon or Ling 3.1 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch Gemini 4 Carbon vs Ling 3.1 Flash