MiMo-V2.6-Pro vs Qwen3.6-35B-A3B

Updated November 5, 2026. Rank says MiMo-V2.6-Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

MiMo-V2.6-Pro

Xiaomi

57.27/100

Estimated · Public rank #58

Conditional range 47.5–67.0

Model B

Qwen3.6-35B-A3B

Alibaba

43.13/100

Estimated · Public rank #126

Conditional range 28.8–57.5

Shared results: 0
MiMo-V2.6-Pro only: 14
Qwen3.6-35B-A3B only: 44
Like-for-like categories: 0 / 8

Estimated: MiMo-V2.6-Pro and Qwen3.6-35B-A3B. 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
MiMo-V2.6-Pro
MiMo-V2.6-Pro 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
Qwen3.6-35B-A3B 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-V2.6-Pro and Qwen3.6-35B-A3B 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
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.

57.1MiMo-V2.6-Pro35.3Qwen3.6-35B-A3B

Directional only · BenchAlign v5.8

MiMo-V2.6-Pro 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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.

Agentic

Directional only

MiMo-V2.6-Pro: 56.6; Estimated · #40/135
Qwen3.6-35B-A3B: 34.3; Estimated · #96/135
Basis: BenchAlign v5.8 lane · 9 vs 11 public rows
Reading: Directional only

Coding

Directional only

MiMo-V2.6-Pro: 57.1; Supported · #25/156
Qwen3.6-35B-A3B: 35.3; Estimated · #82/156
Basis: BenchAlign v5.8 lane · 4 vs 6 public rows
Reading: Directional only

Knowledge

Directional only

MiMo-V2.6-Pro: 62.3; Supported · #42/177
Qwen3.6-35B-A3B: 45.3; Estimated · #98/177
Basis: BenchAlign v5.8 lane · 0 vs 5 public rows
Reading: Directional only

Reasoning

Not comparable

MiMo-V2.6-Pro: 82.5; Unranked · 2 rankable rows
Qwen3.6-35B-A3B: 74.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

MiMo-V2.6-Pro: Not ranked
Qwen3.6-35B-A3B: 66.4; #44/58
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

MiMo-V2.6-Pro: Not ranked
Qwen3.6-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

MiMo-V2.6-Pro: Not ranked
Qwen3.6-35B-A3B: 78.3; #65/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

MiMo-V2.6-Pro: Not ranked
Qwen3.6-35B-A3B: 58.4; #10/12
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for MiMo-V2.6-Pro and Qwen3.6-35B-A3B
Category	MiMo-V2.6-Pro	Qwen3.6-35B-A3B	Basis	Reading
Agentic	56.6Estimated · #40/135	34.3Estimated · #96/135	Directional onlyBenchAlign v5.8 lane · 9 vs 11 public rows	Directional only
Coding	57.1Supported · #25/156	35.3Estimated · #82/156	Directional onlyBenchAlign v5.8 lane · 4 vs 6 public rows	Directional only
Knowledge	62.3Supported · #42/177	45.3Estimated · #98/177	Directional onlyBenchAlign v5.8 lane · 0 vs 5 public rows	Directional only
Reasoning	82.5Unranked · 2 rankable rows	74.3Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	66.4#44/58	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	78.3#65/157	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	58.4#10/12	Not comparableProvisional lane · 0 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

MiMo-V2.6-Pro: $0.00087; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request

Qwen3.6-35B-A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiMo-V2.6-Pro: $0.02436; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request

Qwen3.6-35B-A3B has no comparable published API token rate.

Cache-heavy agent loop

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

MiMo-V2.6-Pro: $0.01812; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.6-35B-A3B 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.

SpecificationMiMo-V2.6-ProQwen3.6-35B-A3B

Context window

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

MiMo-V2.6-Pro

Xiaomi MiMo-V2.6 launch

Qwen3.6-35B-A3B

262K

API model ID

MiMo-V2.6-Pro

Not sourced

Qwen3.6-35B-A3B

Not sourced

Cached-input rate

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

MiMo-V2.6-Pro

$0.0036 per 1M cached input tokens

Xiaomi MiMo-V2.6 launch

Qwen3.6-35B-A3B

No comparable hosted API rate

Documented inputs

MiMo-V2.6-Pro

Not sourced

Qwen3.6-35B-A3B

Not sourced

Documented outputs

MiMo-V2.6-Pro

Not sourced

Qwen3.6-35B-A3B

Not sourced

Provider availability

MiMo-V2.6-Pro

Not sourced

Qwen3.6-35B-A3B

Not sourced

Reasoning profile

MiMo-V2.6-Pro

Reasoning

Qwen3.6-35B-A3B

Reasoning

Weight access

MiMo-V2.6-Pro

Open Weight

Qwen3.6-35B-A3B

Open Weight

License

MiMo-V2.6-Pro

Open Weight

Qwen3.6-35B-A3B

Open Weight

Release date

MiMo-V2.6-Pro

2026-09-22

Qwen3.6-35B-A3B

2026-04-15

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: MiMo-V2.6-Pro has the larger documented window (1M).

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

Questions

Which is better, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

MiMo-V2.6-Pro scores higher for coding on the public lane, 57.1 to 35.3. Qwen3.6-35B-A3B 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-V2.6-Pro or Qwen3.6-35B-A3B?

MiMo-V2.6-Pro scores higher for agentic tasks on the public lane, 56.6 to 34.3. MiMo-V2.6-Pro and Qwen3.6-35B-A3B 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, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

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-V2.6-Pro or Qwen3.6-35B-A3B?

MiMo-V2.6-Pro has the larger documented context window: 1M, compared with 262K.

Self-host vs API cost

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

MiMo-V2.6-Pro

API / mo$979

Self-host / mo$37,843

Break-even2.2B/day

Qwen3.6-35B-A3B

API / mo$0

Self-host / mo$298

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 evidence58 rows

Agentic

Toolathlon-Verified
MiMo-V2.6-Pro76.9%
Source
Qwen3.6-35B-A3B—
Not directly comparable
AutomationBench
MiMo-V2.6-Pro53.1%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Agents' Last Exam
MiMo-V2.6-Pro31.6%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 4.0
MiMo-V2.6-Pro34.90%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 2.1
MiMo-V2.6-Pro89.9%
Source
Qwen3.6-35B-A3B—
Not directly comparable
OSWorld-Verified
MiMo-V2.6-Pro82%
Source
Qwen3.6-35B-A3B—
Not directly comparable
JobBench
MiMo-V2.6-Pro62.0%
Source
Qwen3.6-35B-A3B—
Not directly comparable
CyberGym
MiMo-V2.6-Pro94.0%
Source
Qwen3.6-35B-A3B—
Not directly comparable
ExploitGym
MiMo-V2.6-Pro17.8%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 2.0
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B51.5%
Source
Not directly comparable
Claw-Eval
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B68.7%
Source
Not directly comparable
QwenClawBench
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B52.6%
Source
Not directly comparable
QwenWebBench
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B1397
Source
Not directly comparable
τ³-bench results
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B67.2%
Source
Not directly comparable
VITA-Bench
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B35.6%
Source
Not directly comparable
DeepPlanning
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B25.9%
Source
Not directly comparable
Toolathlon
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B26.9%
Source
Not directly comparable
MCP Atlas
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B62.8%
Source
Not directly comparable
WideResearch
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B60.1%
Source
Not directly comparable
Gert Labs
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B42.65%
Source
Not directly comparable

Coding

DeepSWE
MiMo-V2.6-Pro71.9%
Source
Qwen3.6-35B-A3B—
Not directly comparable
ProgramBench
MiMo-V2.6-Pro26.5%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 2.1
MiMo-V2.6-Pro89.9%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Bug Hunt Bench
MiMo-V2.6-Pro22.7 fixes
Source
Qwen3.6-35B-A3B—
Not directly comparable
SWE-bench Verified
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B73.4%
Source
Not directly comparable
SWE Multilingual
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B67.2%
Source
Not directly comparable
SWE-bench Pro
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B49.5%
Source
Not directly comparable
Terminal-Bench 2.0
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B51.5%
Source
Not directly comparable
LiveCodeBench v6
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B80.4%
Source
Not directly comparable
NL2Repo
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B29.4%
Source
Not directly comparable

Reasoning

STEMBench
MiMo-V2.6-Pro2.17
Source
Qwen3.6-35B-A3B—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B88.11%
Source
Not directly comparable
ParseBench
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B37.35%
Source
Not directly comparable
MMMU
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B81.7%
Source
Not directly comparable
MMMU-Pro
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B75.3%
Source
Not directly comparable
RealWorldQA
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B85.3%
Source
Not directly comparable
OmniDocBench 1.5
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B89.9%
Source
Not directly comparable
CharXiv
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B78%
Source
Not directly comparable
SimpleVQA
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B58.9%
Source
Not directly comparable
CC-OCR
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B81.9%
Source
Not directly comparable
AI2D_TEST
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B92.7%
Source
Not directly comparable
RefCOCO (avg)
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B92.0%
Source
Not directly comparable
ODINW13
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B50.8%
Source
Not directly comparable
Video-MME (with subtitle)
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B86.6%
Source
Not directly comparable
Video-MME (w/o subtitle)
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B82.5%
Source
Not directly comparable
VideoMMMU
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B83.7%
Source
Not directly comparable
MLVU (M-Avg)
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B86.2%
Source
Not directly comparable

Knowledge

MMLU-Pro
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B85.2%
Source
Not directly comparable
SuperGPQA
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B64.7%
Source
Not directly comparable
C-Eval
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B90%
Source
Not directly comparable
GPQA
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B86%
Source
Not directly comparable
HLE
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B21.4%
Source
Not directly comparable

Math

HMMT Feb 2025
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B90.7%
Source
Not directly comparable
HMMT Nov 2025
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B89.1%
Source
Not directly comparable
HMMT Feb 2026
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B83.6%
Source
Not directly comparable
MMAnswerBench
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B78.9%
Source
Not directly comparable
AIME26
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B92.7%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
MiMo-V2.6-Pro—
Qwen3.6-35B-A3B20.351%
Source
Not directly comparable

58 public results · 0 shared

Related comparisons

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Last updated November 5, 2026

MiMo-V2.6-Pro vs Qwen3.6-35B-A3B

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

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

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, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

Which is better for coding, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

Which is better for agentic tasks, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

Which costs less, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

Which has the larger context window, MiMo-V2.6-Pro or Qwen3.6-35B-A3B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch MiMo-V2.6-Pro vs Qwen3.6-35B-A3B