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Thinking Machines Lab logo
Model A
Inkling

Thinking Machines Lab

67.02/100

Supported · Public rank #31

90% interval 60.0–74.0

Inkling vs Mellum2-12B-A2.5B-Thinking

Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

JetBrains logo
Model B
Mellum2-12B-A2.5B-Thinking

JetBrains

Evidence status unavailable

90% interval unavailable

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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. Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. Mellum2-12B-A2.5B-Thinking 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.

Shared results
2
Inkling only
13
Mellum2-12B-A2.5B-Thinking only
4
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Directional only
Inkling
51.6
Mellum2-12B-A2.5B-Thinking
57.6
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Inkling
69.4
Mellum2-12B-A2.5B-Thinking
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Inkling
68.6
Mellum2-12B-A2.5B-Thinking
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Inkling
Not measured
Mellum2-12B-A2.5B-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Inkling
97.1
Mellum2-12B-A2.5B-Thinking
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Inkling
Not measured
Mellum2-12B-A2.5B-Thinking
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Inkling
76.5
Mellum2-12B-A2.5B-Thinking
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Inkling
79.8
Mellum2-12B-A2.5B-Thinking
76.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

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.

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

Inkling
$0.00421
Fits in one request
Mellum2-12B-A2.5B-Thinking
API rate not published
Fits in one request

Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Inkling
$0.10754
Fits in one request
Mellum2-12B-A2.5B-Thinking
API rate not published
Fits in one request

Mellum2-12B-A2.5B-Thinking has no comparable published API token rate.

Cache-heavy agent loop

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

Inkling
$0.159
Fits in one request
Mellum2-12B-A2.5B-Thinking
API rate not published
Does not fit in one request
Cached-input rate unavailable

Mellum2-12B-A2.5B-Thinking does not fit this workload in one request. Mellum2-12B-A2.5B-Thinking 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.

Context window

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

Inkling

1M

Mellum2-12B-A2.5B-Thinking

128K

API model ID

Inkling

Not sourced

Mellum2-12B-A2.5B-Thinking

Not sourced

Cached-input rate

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

Inkling

$0.374 per 1M cached input tokens

Mellum2-12B-A2.5B-Thinking

No comparable hosted API rate

Documented inputs

Inkling

Not sourced

Mellum2-12B-A2.5B-Thinking

Not sourced

Documented outputs

Inkling

Not sourced

Mellum2-12B-A2.5B-Thinking

Not sourced

Provider availability

Inkling

Not sourced

Mellum2-12B-A2.5B-Thinking

Not sourced

Reasoning profile

Inkling

Hybrid

Mellum2-12B-A2.5B-Thinking

Reasoning

Weight access

Inkling

Open Weight

Mellum2-12B-A2.5B-Thinking

Open Weight

License

Inkling

Open Weight

Mellum2-12B-A2.5B-Thinking

Open Weight

Release date

Inkling

2026-07-15

Mellum2-12B-A2.5B-Thinking

2026-05-28

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
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.

Benchmark evidence

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

Browse raw public benchmark evidence19 rows

Agentic

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • BrowseComp

    Inkling77.1%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • MCP Atlas

    Inkling74.1%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • BFCL v4

    Inkling
    Mellum2-12B-A2.5B-Thinking45.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Inkling77.6%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • SWE-bench Pro

    Inkling54.3%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • LiveCodeBench v6

    Inkling
    Mellum2-12B-A2.5B-Thinking69.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Inkling87.9%
    Source
    Mellum2-12B-A2.5B-Thinking57.6%
    Source

    Inkling leads this result

  • GPQA-D

    Inkling87.9%
    Source
    Mellum2-12B-A2.5B-Thinking57.6%
    Source

    Inkling leads this result

  • HLE

    Inkling46%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • HLE w/o tools

    Inkling30%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • MMLU-Redux

    Inkling
    Mellum2-12B-A2.5B-Thinking86.2%
    Source

    Not directly comparable

Math

  • AIME26

    Inkling97.1%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

Multimodal

  • MMMU-Pro

    Inkling73.5%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • CharXiv

    Inkling82%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • CharXiv w/o tools

    Inkling78.1%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

Instruction following

  • IFBench

    Inkling79.8%
    Source
    Mellum2-12B-A2.5B-Thinking

    Not directly comparable

  • IFEval

    Inkling
    Mellum2-12B-A2.5B-Thinking76.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Inkling or Mellum2-12B-A2.5B-Thinking?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Inkling or Mellum2-12B-A2.5B-Thinking?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Inkling or Mellum2-12B-A2.5B-Thinking?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Inkling or Mellum2-12B-A2.5B-Thinking?

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, Inkling or Mellum2-12B-A2.5B-Thinking?

Inkling has the larger documented context window: 1M, compared with 128K.

Related comparisons

Last updated August 29, 2026

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