Model A

Xiaomi

60.72/100

Estimated · Public rank #68

90% interval 47.8–72.2

MiMo-V2.5 vs Qwen3.6-27B

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

Model B

Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.0–58.5

Decision reading

MiMo-V2.5 has the higher public score estimate, 60.72 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

8 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
MiMo-V2.5
MiMo-V2.5 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
MiMo-V2.5 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
MiMo-V2.5 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
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.

7 MiMo-V2.5 only

8 Shared

30 Qwen3.6-27B only

Shared results: 8
MiMo-V2.5 only: 7
Qwen3.6-27B only: 30
Like-for-like categories: 1 / 8

3 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.

Multimodal

Like-for-like

MiMo-V2.5: 58.5; #29/48
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: MiMo-V2.5 leads

Agentic

Directional only

MiMo-V2.5: 51.1; Estimated · #58/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 6 vs 6 public rows
Reading: Directional only

Coding

Directional only

MiMo-V2.5: 54.8; Estimated · #45/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 4 vs 6 public rows
Reading: Directional only

Knowledge

Directional only

MiMo-V2.5: 53.8; Estimated · #65/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 2 vs 6 public rows
Reading: Directional only

Reasoning

Not comparable

MiMo-V2.5: Not ranked
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

MiMo-V2.5: Not ranked
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

MiMo-V2.5: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

MiMo-V2.5: Not ranked
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for MiMo-V2.5 and Qwen3.6-27B
Category	MiMo-V2.5	Qwen3.6-27B	Basis	Reading
Multimodal	58.5#29/48	51.5#35/48	Like-for-likeProvisional lane · 2 vs 2 weighted rows	MiMo-V2.5 leads
Agentic	51.1Estimated · #58/151	33.9Supported · #133/151	Directional onlyBenchAlign lane · 6 vs 6 public rows	Directional only
Coding	54.8Estimated · #45/183	42.6Supported · #128/183	Directional onlyBenchAlign lane · 4 vs 6 public rows	Directional only
Knowledge	53.8Estimated · #65/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 2 vs 6 public rows	Directional only
Reasoning	Not ranked	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	82.2#50/120	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.

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.

Terminal-Bench 2.0
Agentic
MiMo-V2.5: 65.8%Qwen3.6-27B: 59.3%Normalized gap 6.5MiMo-V2.5 source Qwen3.6-27B source
SWE-bench Pro
Coding
MiMo-V2.5: 56.1%Qwen3.6-27B: 53.5%Normalized gap 2.6MiMo-V2.5 source Qwen3.6-27B source
CharXiv
Multimodal
MiMo-V2.5: 81%Qwen3.6-27B: 78.4%Normalized gap 2.6MiMo-V2.5 source Qwen3.6-27B source
MMMU-Pro
Multimodal
MiMo-V2.5: 77.9%Qwen3.6-27B: 75.8%Normalized gap 2.1MiMo-V2.5 source Qwen3.6-27B 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

MiMo-V2.5: API rate not published; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

MiMo-V2.5 has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiMo-V2.5: API rate not published; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

MiMo-V2.5 has no comparable published API token rate. Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

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

MiMo-V2.5: API rate not published; Fits in one request; Cached-input rate unavailable
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

MiMo-V2.5 has no comparable published API token rate. Qwen3.6-27B 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.

SpecificationMiMo-V2.5Qwen3.6-27B

Context window

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

MiMo-V2.5

Qwen3.6-27B

262K

API model ID

MiMo-V2.5

Not sourced

Qwen3.6-27B

Not sourced

Cached-input rate

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

MiMo-V2.5

No comparable hosted API rate

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

MiMo-V2.5

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

MiMo-V2.5

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

MiMo-V2.5

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

MiMo-V2.5

Reasoning

Qwen3.6-27B

Reasoning

Weight access

MiMo-V2.5

Proprietary

Qwen3.6-27B

Open Weight

License

MiMo-V2.5

Proprietary

Qwen3.6-27B

Open Weight

Release date

MiMo-V2.5

2026-04-22

Qwen3.6-27B

2026-04-21

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: MiMo-V2.5 has the higher public score estimate, 60.72 versus 52.73, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: MiMo-V2.5 has the larger documented window (1M).

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.

MiMo-V2.5

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-27B

API / mo$0

Self-host / mo$429

Break-even—

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 evidence45 rows

Agentic

Claw-Eval
MiMo-V2.562.3%
Source
Qwen3.6-27B72.4%
Source
Qwen3.6-27B leads this result
MM-ClawBench
MiMo-V2.523.8%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
MiMo-V2.565.8%
Source
Qwen3.6-27B59.3%
Source
MiMo-V2.5 leads this result
Gert Labs
Shared source
MiMo-V2.546.89%
Qwen3.6-27B54.84%
Qwen3.6-27B leads this result
ResearchClawBench
MiMo-V2.516.9%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
MiMo-V2.560.7%
Source
Qwen3.6-27B—
Not directly comparable
QwenClawBench
MiMo-V2.5—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
MiMo-V2.5—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
MiMo-V2.5—
Qwen3.6-27B70.3%
Source
Not directly comparable

Coding

SWE-bench Pro
MiMo-V2.556.1%
Source
Qwen3.6-27B53.5%
Source
MiMo-V2.5 leads this result
Terminal-Bench 2.0
MiMo-V2.565.8%
Source
Qwen3.6-27B59.3%
Source
MiMo-V2.5 leads this result
LiveCodeBench (Vals)
MiMo-V2.581.5%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
MiMo-V2.571.0%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
MiMo-V2.5—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
MiMo-V2.5—
Qwen3.6-27B71.3%
Source
Not directly comparable
LiveCodeBench
MiMo-V2.5—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
MiMo-V2.5—
Qwen3.6-27B36.2%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
MiMo-V2.581.6%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
MiMo-V2.582.9%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
MiMo-V2.5—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
MiMo-V2.5—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
MiMo-V2.5—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
MiMo-V2.5—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
MiMo-V2.5—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
MiMo-V2.5—
Qwen3.6-27B24%
Source
Not directly comparable

Math

HMMT Feb 2025
MiMo-V2.5—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
MiMo-V2.5—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
MiMo-V2.5—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
MiMo-V2.5—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
MiMo-V2.5—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

Video-MME (with subtitle)
MiMo-V2.587.7%
Source
Qwen3.6-27B87.7%
Source
Tie
CharXiv
MiMo-V2.581%
Source
Qwen3.6-27B78.4%
Source
MiMo-V2.5 leads this result
MMMU-Pro
MiMo-V2.577.9%
Source
Qwen3.6-27B75.8%
Source
MiMo-V2.5 leads this result
MMMU
MiMo-V2.5—
Qwen3.6-27B82.9%
Source
Not directly comparable
RealWorldQA
MiMo-V2.5—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
MiMo-V2.5—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
MiMo-V2.5—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
MiMo-V2.5—
Qwen3.6-27B56.1%
Source
Not directly comparable
CC-OCR
MiMo-V2.5—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
MiMo-V2.5—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
MiMo-V2.5—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
MiMo-V2.5—
Qwen3.6-27B62.5%
Source
Not directly comparable
VideoMMMU
MiMo-V2.5—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
MiMo-V2.5—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
MiMo-V2.5—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, MiMo-V2.5 or Qwen3.6-27B?

MiMo-V2.5 has the higher public score estimate, 60.72 versus 52.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, MiMo-V2.5 or Qwen3.6-27B?

MiMo-V2.5 scores higher for coding on the public lane, 54.8 to 42.6. MiMo-V2.5 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, MiMo-V2.5 or Qwen3.6-27B?

MiMo-V2.5 scores higher for agentic tasks on the public lane, 51.1 to 33.9. MiMo-V2.5 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, MiMo-V2.5 or Qwen3.6-27B?

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, MiMo-V2.5 or Qwen3.6-27B?

MiMo-V2.5 has the larger documented context window: 1M, compared with 262K.

Related comparisons

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Last updated September 4, 2026

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MiMo-V2.5 vs Qwen3.6-27B

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

Multimodal

Agentic

Coding

Knowledge

Reasoning

Math

Multilingual

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, MiMo-V2.5 or Qwen3.6-27B?

Which is better for coding, MiMo-V2.5 or Qwen3.6-27B?

Which is better for agentic tasks, MiMo-V2.5 or Qwen3.6-27B?

Which costs less, MiMo-V2.5 or Qwen3.6-27B?

Which has the larger context window, MiMo-V2.5 or Qwen3.6-27B?

Related comparisons

Watch MiMo-V2.5 vs Qwen3.6-27B