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BenchLM
Anthropic logo
Model A
Claude Sonnet 4.6

Anthropic

64.21/100

Supported · Public rank #48

90% interval 54.6–73.8

Claude Sonnet 4.6 vs Mellum2-12B-A2.5B-Instruct

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

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

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.

1 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

    Claude Sonnet 4.6

    Claude Sonnet 4.6 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

    Mellum2-12B-A2.5B-Instruct 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

    Mellum2-12B-A2.5B-Instruct is 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. Claude Sonnet 4.6 does not fit this workload in one request. Mellum2-12B-A2.5B-Instruct does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mellum2-12B-A2.5B-Instruct has no comparable published API token rate.

    Confidence: rate-fallback

  • 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
1
Claude Sonnet 4.6 only
24
Mellum2-12B-A2.5B-Instruct only
5
Like-for-like categories
0 / 8

2 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
Claude Sonnet 4.6
45.0
Supported · #96/151
Mellum2-12B-A2.5B-Instruct
47.8
Estimated · #82/151
Basis
BenchAlign lane · 8 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Claude Sonnet 4.6
56.7
Supported · #51/181
Mellum2-12B-A2.5B-Instruct
48.1
Estimated · #103/181
Basis
BenchAlign lane · 6 vs 3 public rows
Reading
Directional only

Coding

Not comparable
Claude Sonnet 4.6
52.4
Supported · #55/183
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
BenchAlign lane · 8 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Sonnet 4.6
65.8
Unranked · 2 rankable rows
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 4.6
49.0
Unranked · 2 rankable rows
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 4.6
Not ranked
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 4.6
54.1
#33/48
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 4.6
47.9
#84/120
Mellum2-12B-A2.5B-Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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.

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

Claude Sonnet 4.6
$0.0105
Fits in one request
Mellum2-12B-A2.5B-Instruct
API rate not published
Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 4.6
$0.195
Fits in one request
Mellum2-12B-A2.5B-Instruct
API rate not published
Fits in one request

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

Cache-heavy agent loop

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

Claude Sonnet 4.6
$0.81
Does not fit in one request
Cached input priced at the published list-input rate
Mellum2-12B-A2.5B-Instruct
API rate not published
Does not fit in one request
Cached-input rate unavailable

Claude Sonnet 4.6 does not fit this workload in one request. Mellum2-12B-A2.5B-Instruct does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mellum2-12B-A2.5B-Instruct 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.

Claude Sonnet 4.6

200K

Mellum2-12B-A2.5B-Instruct

128K

API model ID

Claude Sonnet 4.6

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Cached-input rate

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

Claude Sonnet 4.6

Not published

Mellum2-12B-A2.5B-Instruct

No comparable hosted API rate

Documented inputs

Claude Sonnet 4.6

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Documented outputs

Claude Sonnet 4.6

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Provider availability

Claude Sonnet 4.6

Not sourced

Mellum2-12B-A2.5B-Instruct

Not sourced

Reasoning profile

Claude Sonnet 4.6

Non-Reasoning

Mellum2-12B-A2.5B-Instruct

Non-Reasoning

Weight access

Claude Sonnet 4.6

Proprietary

Mellum2-12B-A2.5B-Instruct

Open Weight

License

Claude Sonnet 4.6

Proprietary

Mellum2-12B-A2.5B-Instruct

Open Weight

Release date

Claude Sonnet 4.6

2026-02-01

Mellum2-12B-A2.5B-Instruct

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
Claude Sonnet 4.6 has the larger documented window (200K).

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 evidence30 rows

Agentic

  • Terminal-Bench 2.0

    Claude Sonnet 4.659.1%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 4.672.1%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • Claw-Eval

    Claude Sonnet 4.667.8%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • CyberGym

    Claude Sonnet 4.665.2%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • Gert Labs

    Claude Sonnet 4.662.92%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • OSWorld 2.0

    Claude Sonnet 4.68.3%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • JobBench

    Claude Sonnet 4.636.9%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Sonnet 4.657.3%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • BFCL v4

    Claude Sonnet 4.6—
    Mellum2-12B-A2.5B-Instruct44.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 4.679.6%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • SWE-Rebench

    Claude Sonnet 4.660.7%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • React Native Evals

    Claude Sonnet 4.680.6%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • Vibe Code Bench

    Claude Sonnet 4.651.48%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • cursorBench31

    Claude Sonnet 4.648.8%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Sonnet 4.624.3%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Sonnet 4.682.1%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Sonnet 4.677.4%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • LiveCodeBench v6

    Claude Sonnet 4.6—
    Mellum2-12B-A2.5B-Instruct37.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Sonnet 4.689.9%
    Source
    Mellum2-12B-A2.5B-Instruct40.9%
    Source

    Claude Sonnet 4.6 leads this result

  • SuperGPQA

    Claude Sonnet 4.695%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • MMLU-Pro

    Claude Sonnet 4.679.2%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • HLE

    Claude Sonnet 4.649%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Sonnet 4.685.6%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Sonnet 4.687.3%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • MMLU-Redux

    Claude Sonnet 4.6—
    Mellum2-12B-A2.5B-Instruct78.1%
    Source

    Not directly comparable

  • GPQA-D

    Claude Sonnet 4.6—
    Mellum2-12B-A2.5B-Instruct40.9%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Sonnet 4.632.400%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Sonnet 4.68.300%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

Multimodal

  • CharXiv

    Claude Sonnet 4.677.4%
    Source
    Mellum2-12B-A2.5B-Instruct—

    Not directly comparable

Instruction following

  • IFEval

    Claude Sonnet 4.6—
    Mellum2-12B-A2.5B-Instruct75.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 4.6 or Mellum2-12B-A2.5B-Instruct?

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, Claude Sonnet 4.6 or Mellum2-12B-A2.5B-Instruct?

Mellum2-12B-A2.5B-Instruct is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Sonnet 4.6 or Mellum2-12B-A2.5B-Instruct?

Mellum2-12B-A2.5B-Instruct scores higher for agentic tasks on the public lane, 47.8 to 45. Mellum2-12B-A2.5B-Instruct is 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, Claude Sonnet 4.6 or Mellum2-12B-A2.5B-Instruct?

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, Claude Sonnet 4.6 or Mellum2-12B-A2.5B-Instruct?

Claude Sonnet 4.6 has the larger documented context window: 200K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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