Gemma 4 12B vs Inkling

Updated October 14, 2026. Rank says Inkling is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Inkling has the higher public point estimate, 52.13 versus 36.69. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 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 12B

Google

36.69/100

Estimated · Public rank #146

Conditional range 22.3–51.0

Model B

Inkling

Thinking Machines Lab

52.13/100

Supported · Public rank #85

90% interval 41.7–62.5

Shared results: 5
Gemma 4 12B only: 6
Inkling only: 19
Like-for-like categories: 1 / 8

Estimated: Gemma 4 12B · Supported: Inkling. 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
Inkling
Inkling 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
Gemma 4 12B 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
Gemma 4 12B is not ranked on the public lane for agentic, so no winner is named for agentic.
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.

23.2Gemma 4 12B34.0Inkling

Directional only · BenchAlign v5.8

Inkling 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.

HLE w/o toolsKnowledge
Normalized gap 24.8
Gemma 4 12B:5.2%
Inkling:30%
Gemma 4 12B source Inkling source
AIME26Math
Normalized gap 19.6
Gemma 4 12B:77.5%
Inkling:97.1%
Gemma 4 12B source Inkling source
GPQAKnowledge
Normalized gap 9.1
Gemma 4 12B:78.8%
Inkling:87.9%
Gemma 4 12B source Inkling source
MMMU-ProMultimodal
Normalized gap 4.4
Gemma 4 12B:69.1%
Inkling:73.5%
Gemma 4 12B source Inkling 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.

Knowledge

Like-for-like

Gemma 4 12B: 35.8; Supported · #130/181
Inkling: 54.5; Supported · #62/181
Basis: BenchAlign v5.8 lane · 5 vs 6 public rows
Reading: Inkling leads · intervals overlap

Coding

Directional only

Gemma 4 12B: 23.2; Estimated · #111/149
Inkling: 34.0; Supported · #80/149
Basis: BenchAlign v5.8 lane · 1 vs 6 public rows
Reading: Directional only

Multimodal

Directional only

Gemma 4 12B: 59.4; #44/54
Inkling: 67.5; #35/54
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 12B: 90.1; #25/128
Inkling: 87.6; #35/128
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 12B: Not ranked
Inkling: 36.2; Supported · #72/124
Basis: BenchAlign v5.8 lane · 0 vs 5 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 12B: 52.6; Unranked · 3 rankable rows
Inkling: 79.0; #14/29
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

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

Math

Not comparable

Gemma 4 12B: 61.3; Unranked · 1 rankable row
Inkling: 58.3; #11/12
Basis: Provisional lane · 1 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 12B and Inkling
Category	Gemma 4 12B	Inkling	Basis	Reading
Knowledge	35.8Supported · #130/181	54.5Supported · #62/181	Like-for-likeBenchAlign v5.8 lane · 5 vs 6 public rows	Inkling leads · intervals overlap
Coding	23.2Estimated · #111/149	34.0Supported · #80/149	Directional onlyBenchAlign v5.8 lane · 1 vs 6 public rows	Directional only
Multimodal	59.4#44/54	67.5#35/54	Directional onlyProvisional lane · 1 vs 2 weighted rows	Directional only
Instruction following	90.1#25/128	87.6#35/128	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	Not ranked	36.2Supported · #72/124	Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows	Not comparable
Reasoning	52.6Unranked · 3 rankable rows	79.0#14/29	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	61.3Unranked · 1 rankable row	58.3#11/12	Not comparableProvisional lane · 1 vs 3 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 12B: API rate not published; Fits in one request
Inkling: $0.00421; Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Gemma 4 12B: API rate not published; Fits in one request
Inkling: $0.10754; 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

Gemma 4 12B: API rate not published; Fits in one request; Cached-input rate unavailable
Inkling: $0.159; Fits in one request

Gemma 4 12B 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 12BInkling

Context window

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

Gemma 4 12B

256K

Google Gemma 4 model documentation

Inkling

Gemma 4 12B

Reasoning

Inkling

Hybrid

Weight access

Gemma 4 12B

Open Weight

Inkling

Open Weight

License

Gemma 4 12B

Open Weight

Inkling

Open Weight

Release date

Gemma 4 12B

2026-06-03

Inkling

2026-07-15

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: Inkling has the higher public point estimate, 52.13 versus 36.69. 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: Inkling has the larger documented window (1M).

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

Questions

Which is better, Gemma 4 12B or Inkling?

Inkling has the higher public point estimate, 52.13 versus 36.69. 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 12B or Inkling?

Inkling scores higher for coding on the public lane, 34 to 23.2. Gemma 4 12B 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, Gemma 4 12B or Inkling?

Gemma 4 12B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 12B or Inkling?

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 12B or Inkling?

Inkling has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence30 rows

Agentic

Terminal-Bench 2.1
Gemma 4 12B—
Inkling63.8%
Source
Not directly comparable
BrowseComp
Gemma 4 12B—
Inkling77.1%
Source
Not directly comparable
MCP Atlas
Gemma 4 12B—
Inkling74.1%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 12B—
Inkling47.6%
Source
Not directly comparable
CWE-bench v1
Gemma 4 12B—
Inkling37.0%
Source
Not directly comparable

Coding

LiveCodeBench v6
Gemma 4 12B72.0%
Source
Inkling—
Not directly comparable
SWE-bench Verified
Gemma 4 12B—
Inkling77.6%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 12B—
Inkling54.3%
Source
Not directly comparable
Terminal-Bench 2.1
Gemma 4 12B—
Inkling63.8%
Source
Not directly comparable
FrontierSWE v2
Gemma 4 12B—
Inkling4.1%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 12B—
Inkling85.5%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 12B—
Inkling77.6%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 12B43.4%
Source
Inkling—
Not directly comparable

Multimodal

MMMU-Pro
Gemma 4 12B69.1%
Source
Inkling73.5%
Source
Inkling leads this result
MathVision
Gemma 4 12B79.7%
Source
Inkling—
Not directly comparable
MedXpertQA (MM)
Gemma 4 12B48.7%
Source
Inkling—
Not directly comparable
CharXiv
Gemma 4 12B—
Inkling82%
Source
Not directly comparable
CharXiv w/o tools
Gemma 4 12B—
Inkling78.1%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 12B78.8%
Source
Inkling87.9%
Source
Inkling leads this result
GPQA-D
Gemma 4 12B78.8%
Source
Inkling87.9%
Source
Inkling leads this result
MMLU-Pro
Gemma 4 12B77.2%
Source
Inkling—
Not directly comparable
HLE w/o tools
Gemma 4 12B5.2%
Source
Inkling30%
Source
Inkling leads this result
MMMLU
Gemma 4 12B83.4%
Source
Inkling—
Not directly comparable
HLE
Gemma 4 12B—
Inkling46%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 12B—
Inkling87.1%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 12B—
Inkling86.3%
Source
Not directly comparable

Instruction following

IFBench
Gemma 4 12B—
Inkling79.8%
Source
Not directly comparable

Math

AIME26
Gemma 4 12B77.5%
Source
Inkling97.1%
Source
Inkling leads this result
FrontierMath v2 (Tiers 1-3)
Gemma 4 12B—
Inkling33.333%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemma 4 12B—
Inkling4.878%
Source
Not directly comparable

30 public results · 5 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch Gemma 4 12B vs Inkling

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,500+ readers.

Last updated October 14, 2026

Gemma 4 12B vs Inkling

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

Knowledge

Coding

Multimodal

Instruction following

Agentic

Reasoning

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 12B or Inkling?

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

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

Which costs less, Gemma 4 12B or Inkling?

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemma 4 12B vs Inkling