Model A

NVIDIA

—

Evidence status unavailable

90% interval unavailable

Cosmos3-Edge vs Gemma 4 12B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Gemma 4 12B

Google

46.65/100

Estimated · Public rank #161

90% interval 35.1–58.2

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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
Cosmos3-Edge 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
Cosmos3-Edge 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
No clear pick
The documented context windows are equal.
Confidence: documented
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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

12 Gemma 4 12B only

Shared results: 0
Cosmos3-Edge only: 0
Gemma 4 12B only: 12
Like-for-like categories: 0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Agentic

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 47.9; Estimated · #81/151
Basis: BenchAlign lane · 0 vs 0 public rows
Reading: Not comparable

Coding

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 46.6; Estimated · #97/183
Basis: BenchAlign lane · 0 vs 1 public rows
Reading: Not comparable

Reasoning

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 34.0; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 40.6; Supported · #138/181
Basis: BenchAlign lane · 0 vs 5 public rows
Reading: Not comparable

Math

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 57.6; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 26.1; #44/48
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Cosmos3-Edge: Not ranked
Gemma 4 12B: 89.8; #21/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Cosmos3-Edge and Gemma 4 12B
Category	Cosmos3-Edge	Gemma 4 12B	Basis	Reading
Agentic	Not ranked	47.9Estimated · #81/151	Not comparableBenchAlign lane · 0 vs 0 public rows	Not comparable
Coding	Not ranked	46.6Estimated · #97/183	Not comparableBenchAlign lane · 0 vs 1 public rows	Not comparable
Reasoning	Not ranked	34.0Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Knowledge	Not ranked	40.6Supported · #138/181	Not comparableBenchAlign lane · 0 vs 5 public rows	Not comparable
Math	Not ranked	57.6Unranked · 1 rankable row	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
Multimodal	Not ranked	26.1#44/48	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	89.8#21/120	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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Cosmos3-Edge: Self-hosted; infrastructure cost varies; Fits in one request
Gemma 4 12B: API rate not published; Fits in one request

Cosmos3-Edge has no comparable published API token rate. Gemma 4 12B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Cosmos3-Edge: Self-hosted; infrastructure cost varies; Fits in one request
Gemma 4 12B: API rate not published; Fits in one request

Cosmos3-Edge has no comparable published API token rate. Gemma 4 12B has no comparable published API token rate.

Cache-heavy agent loop

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

Cosmos3-Edge: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Gemma 4 12B: API rate not published; Fits in one request; Cached-input rate unavailable

Cosmos3-Edge has no comparable published API token rate. Gemma 4 12B has no comparable published API token rate.

Specification differences

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

SpecificationCosmos3-EdgeGemma 4 12B

Context window

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

Cosmos3-Edge

256K

Gemma 4 12B

256K

Google Gemma 4 model documentation

Cosmos3-Edge

Reasoning

Gemma 4 12B

Reasoning

Weight access

Cosmos3-Edge

Open Weight

Gemma 4 12B

Open Weight

License

Cosmos3-Edge

Open Weight

Gemma 4 12B

Open Weight

Release date

Cosmos3-Edge

2026-07-20

Gemma 4 12B

2026-06-03

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 256K.

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

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence12 rows

Coding

LiveCodeBench v6
Cosmos3-Edge—
Gemma 4 12B72.0%
Source
Not directly comparable

Reasoning

BBH
Cosmos3-Edge—
Gemma 4 12B53%
Source
Not directly comparable
MRCRv2
Cosmos3-Edge—
Gemma 4 12B43.4%
Source
Not directly comparable

Knowledge

GPQA
Cosmos3-Edge—
Gemma 4 12B78.8%
Source
Not directly comparable
GPQA-D
Cosmos3-Edge—
Gemma 4 12B78.8%
Source
Not directly comparable
MMLU-Pro
Cosmos3-Edge—
Gemma 4 12B77.2%
Source
Not directly comparable
HLE w/o tools
Cosmos3-Edge—
Gemma 4 12B5.2%
Source
Not directly comparable
MMMLU
Cosmos3-Edge—
Gemma 4 12B83.4%
Source
Not directly comparable

Math

AIME26
Cosmos3-Edge—
Gemma 4 12B77.5%
Source
Not directly comparable

Multimodal

MMMU-Pro
Cosmos3-Edge—
Gemma 4 12B69.1%
Source
Not directly comparable
MathVision
Cosmos3-Edge—
Gemma 4 12B79.7%
Source
Not directly comparable
MedXpertQA (MM)
Cosmos3-Edge—
Gemma 4 12B48.7%
Source
Not directly comparable

Frequently asked questions

Which is better, Cosmos3-Edge or Gemma 4 12B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Cosmos3-Edge or Gemma 4 12B?

Cosmos3-Edge is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Cosmos3-Edge or Gemma 4 12B?

Cosmos3-Edge is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Cosmos3-Edge or Gemma 4 12B?

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, Cosmos3-Edge or Gemma 4 12B?

Both models list the same context window, 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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Cosmos3-Edge vs Gemma 4 12B

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Cosmos3-Edge or Gemma 4 12B?

Which is better for coding, Cosmos3-Edge or Gemma 4 12B?

Which is better for agentic tasks, Cosmos3-Edge or Gemma 4 12B?

Which costs less, Cosmos3-Edge or Gemma 4 12B?

Which has the larger context window, Cosmos3-Edge or Gemma 4 12B?

Related comparisons

Watch Cosmos3-Edge vs Gemma 4 12B