Model A

DeepSeek

43.74/100

Supported · Public rank #175

90% interval 27.7–59.7

DeepSeek V3 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

46.64/100

Estimated · Public rank #161

90% interval 35.1–58.2

Decision reading

Gemma 4 12B has the higher public score estimate, 46.64 versus 43.74, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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.

Long documents
Prompts that approach the documented context limit
Gemma 4 12B
Gemma 4 12B 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 and Gemma 4 12B 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
DeepSeek V3 and Gemma 4 12B 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. DeepSeek V3 does not fit this workload in one request. Gemma 4 12B 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

What is actually comparable

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

4 DeepSeek V3 only

2 Shared

10 Gemma 4 12B only

Shared results: 2
DeepSeek V3 only: 4
Gemma 4 12B only: 10
Like-for-like categories: 0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Directional only

DeepSeek V3: 38.5; Estimated · #124/151
Gemma 4 12B: 47.9; Estimated · #81/151
Basis: BenchAlign lane · 0 vs 0 public rows
Reading: Directional only

Coding

Directional only

DeepSeek V3: 41.1; Estimated · #135/183
Gemma 4 12B: 46.6; Estimated · #97/183
Basis: BenchAlign lane · 2 vs 1 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V3: 40.7; Estimated · #136/181
Gemma 4 12B: 40.4; Supported · #138/181
Basis: BenchAlign lane · 2 vs 5 public rows
Reading: Directional only

Instruction following

Directional only

DeepSeek V3: 39.6; #102/120
Gemma 4 12B: 89.8; #21/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

DeepSeek V3: 43.8; Unranked · 2 rankable rows
Gemma 4 12B: 34.0; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3: 26.2; Unranked · 1 rankable row
Gemma 4 12B: 57.6; Unranked · 1 rankable row
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3: Not ranked
Gemma 4 12B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3: Not ranked
Gemma 4 12B: 26.1; #44/48
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for DeepSeek V3 and Gemma 4 12B
Category	DeepSeek V3	Gemma 4 12B	Basis	Reading
Agentic	38.5Estimated · #124/151	47.9Estimated · #81/151	Directional onlyBenchAlign lane · 0 vs 0 public rows	Directional only
Coding	41.1Estimated · #135/183	46.6Estimated · #97/183	Directional onlyBenchAlign lane · 2 vs 1 public rows	Directional only
Knowledge	40.7Estimated · #136/181	40.4Supported · #138/181	Directional onlyBenchAlign lane · 2 vs 5 public rows	Directional only
Instruction following	39.6#102/120	89.8#21/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	43.8Unranked · 2 rankable rows	34.0Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	26.2Unranked · 1 rankable row	57.6Unranked · 1 rankable row	Not comparableProvisional lane · 1 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

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.

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.

GPQA
Knowledge
DeepSeek V3: 59.1%Gemma 4 12B: 78.8%Normalized gap 19.7DeepSeek V3 source Gemma 4 12B source
MMLU-Pro
Knowledge
DeepSeek V3: 75.9%Gemma 4 12B: 77.2%Normalized gap 1.3DeepSeek V3 source Gemma 4 12B source

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: $0.00082; Fits in one request
Gemma 4 12B: API rate not published; Fits in one request

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

Repository review

50K fresh input + 3K output tokens

DeepSeek V3: $0.0168; Fits in one request
Gemma 4 12B: API rate not published; Fits in one request

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

Cache-heavy agent loop

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

DeepSeek V3: $0.0304; Does not fit in one request
Gemma 4 12B: API rate not published; Fits in one request; Cached-input rate unavailable

DeepSeek V3 does not fit this workload in one request. 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.

SpecificationDeepSeek V3Gemma 4 12B

Context window

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

DeepSeek V3

128K

Gemma 4 12B

256K

Google Gemma 4 model documentation

DeepSeek V3

Non-Reasoning

Gemma 4 12B

Reasoning

Weight access

DeepSeek V3

Open Weight

Gemma 4 12B

Open Weight

License

DeepSeek V3

Open Weight

Gemma 4 12B

Open Weight

Release date

DeepSeek V3

2024-12-26

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: Gemma 4 12B has the higher public score estimate, 46.64 versus 43.74, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemma 4 12B has the larger documented window (256K).

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

Self-host vs API cost

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

DeepSeek V3

API / mo$1,028

Self-host / mo$18,221

Break-even1.2B/day

Gemma 4 12B

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 evidence16 rows

Coding

LiveCodeBench
DeepSeek V337.6%
Source
Gemma 4 12B—
Not directly comparable
SWE-bench Verified
DeepSeek V342%
Source
Gemma 4 12B—
Not directly comparable
LiveCodeBench v6
DeepSeek V3—
Gemma 4 12B72.0%
Source
Not directly comparable

Reasoning

BBH
DeepSeek V3—
Gemma 4 12B53%
Source
Not directly comparable
MRCRv2
DeepSeek V3—
Gemma 4 12B43.4%
Source
Not directly comparable

Knowledge

GPQA
DeepSeek V359.1%
Source
Gemma 4 12B78.8%
Source
Gemma 4 12B leads this result
MMLU-Pro
DeepSeek V375.9%
Source
Gemma 4 12B77.2%
Source
Gemma 4 12B leads this result
GPQA-D
DeepSeek V3—
Gemma 4 12B78.8%
Source
Not directly comparable
HLE w/o tools
DeepSeek V3—
Gemma 4 12B5.2%
Source
Not directly comparable
MMMLU
DeepSeek V3—
Gemma 4 12B83.4%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
DeepSeek V31.724%
Source
Gemma 4 12B—
Not directly comparable
AIME26
DeepSeek V3—
Gemma 4 12B77.5%
Source
Not directly comparable

Multimodal

MMMU-Pro
DeepSeek V3—
Gemma 4 12B69.1%
Source
Not directly comparable
MathVision
DeepSeek V3—
Gemma 4 12B79.7%
Source
Not directly comparable
MedXpertQA (MM)
DeepSeek V3—
Gemma 4 12B48.7%
Source
Not directly comparable

Instruction following

IFEval
DeepSeek V386.1%
Source
Gemma 4 12B—
Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3 or Gemma 4 12B?

Gemma 4 12B has the higher public score estimate, 46.64 versus 43.74, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V3 or Gemma 4 12B?

Gemma 4 12B scores higher for coding on the public lane, 46.6 to 41.1. DeepSeek V3 and Gemma 4 12B 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, DeepSeek V3 or Gemma 4 12B?

Gemma 4 12B scores higher for agentic tasks on the public lane, 47.9 to 38.5. DeepSeek V3 and Gemma 4 12B 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, DeepSeek V3 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, DeepSeek V3 or Gemma 4 12B?

Gemma 4 12B has the larger documented context window: 256K, compared with 128K.

Related comparisons

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Last updated September 4, 2026

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DeepSeek V3 vs Gemma 4 12B

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

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

Self-host vs API cost

Benchmark evidence

Coding

Reasoning

Knowledge

Math

Multimodal

Instruction following

Frequently asked questions

Which is better, DeepSeek V3 or Gemma 4 12B?

Which is better for coding, DeepSeek V3 or Gemma 4 12B?

Which is better for agentic tasks, DeepSeek V3 or Gemma 4 12B?

Which costs less, DeepSeek V3 or Gemma 4 12B?

Which has the larger context window, DeepSeek V3 or Gemma 4 12B?

Related comparisons

Watch DeepSeek V3 vs Gemma 4 12B