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BenchLM

MiMo-V2-Omni vs Qwen3.5-122B-A10B

Updated September 27, 2026. Rank says MiMo-V2-Omni is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

MiMo-V2-Omni has the higher public score estimate, 49.22 versus 40.06, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Xiaomi logo

Xiaomi

49.22/100

Estimated · Public rank #78

90% interval 34.5–64.0

Model B
Alibaba logo

Alibaba

40.06/100

Estimated · Public rank #113

90% interval 19.5–60.6

Shared results
1
MiMo-V2-Omni only
1
Qwen3.5-122B-A10B only
14
Like-for-like categories
0 / 8
Estimated: MiMo-V2-Omni and Qwen3.5-122B-A10BHow 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    MiMo-V2-Omni 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

    MiMo-V2-Omni is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented
  • 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.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—MiMo-V2-Omni35.7Qwen3.5-122B-A10B

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

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 v5.7 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

Directional only
MiMo-V2-Omni
41.7
Estimated · #83/158
Qwen3.5-122B-A10B
41.5
Supported · #86/158
Basis
BenchAlign v5.7 lane · 0 vs 3 public rows
Reading
Directional only

Instruction following

Directional only
MiMo-V2-Omni
62.6
#72/124
Qwen3.5-122B-A10B
91.6
#11/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
MiMo-V2-Omni
Not ranked
Qwen3.5-122B-A10B
23.1
Estimated · #84/105
Basis
BenchAlign v5.7 lane · 1 vs 3 public rows
Reading
Not comparable

Coding

Not comparable
MiMo-V2-Omni
Not ranked
Qwen3.5-122B-A10B
35.7
Supported · #73/135
Basis
BenchAlign v5.7 lane · 1 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
MiMo-V2-Omni
73.6
Unranked · 2 rankable rows
Qwen3.5-122B-A10B
49.8
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
MiMo-V2-Omni
63.1
Unranked · 1 rankable row
Qwen3.5-122B-A10B
57.0
#34/50
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
MiMo-V2-Omni
Not ranked
Qwen3.5-122B-A10B
36.8
#10/12
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
MiMo-V2-Omni
Not ranked
Qwen3.5-122B-A10B
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 v5.7) 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-Omni
API rate not published
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

MiMo-V2-Omni has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiMo-V2-Omni
API rate not published
Fits in one request
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request

MiMo-V2-Omni has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.

Cache-heavy agent loop

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

MiMo-V2-Omni
API rate not published
Fits in one request
Cached-input rate unavailable
Qwen3.5-122B-A10B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

MiMo-V2-Omni has no comparable published API token rate. Qwen3.5-122B-A10B 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.

Context window

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

MiMo-V2-Omni

262K

Qwen3.5-122B-A10B

262K

API model ID

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

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-Omni

No comparable hosted API rate

Qwen3.5-122B-A10B

No comparable hosted API rate

Documented inputs

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Documented outputs

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Provider availability

MiMo-V2-Omni

Not sourced

Qwen3.5-122B-A10B

Not sourced

Reasoning profile

MiMo-V2-Omni

Reasoning

Qwen3.5-122B-A10B

Reasoning

Weight access

MiMo-V2-Omni

Proprietary

Qwen3.5-122B-A10B

Open Weight

License

MiMo-V2-Omni

Proprietary

Qwen3.5-122B-A10B

Open Weight

Release date

MiMo-V2-Omni

2026-03-18

Qwen3.5-122B-A10B

2026-03-04

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-Omni has the higher public score estimate, 49.22 versus 40.06, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 262K.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, MiMo-V2-Omni or Qwen3.5-122B-A10B?

MiMo-V2-Omni has the higher public score estimate, 49.22 versus 40.06, 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-Omni or Qwen3.5-122B-A10B?

MiMo-V2-Omni is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, MiMo-V2-Omni or Qwen3.5-122B-A10B?

MiMo-V2-Omni is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, MiMo-V2-Omni or Qwen3.5-122B-A10B?

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-Omni or Qwen3.5-122B-A10B?

Both models list the same context window, 262K.

Benchmark evidence

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

Browse raw public benchmark evidence16 rows

Agentic

  • Claw-Eval

    MiMo-V2-Omni45.2%
    Source
    Qwen3.5-122B-A10B—

    Not directly comparable

  • Terminal-Bench 2.0

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B49.4%
    Source

    Not directly comparable

  • BrowseComp

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B63.8%
    Source

    Not directly comparable

  • OSWorld-Verified

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B58%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    MiMo-V2-Omni74.8%
    Source
    Qwen3.5-122B-A10B72%
    Source

    MiMo-V2-Omni leads this result

Reasoning

  • LongBench v2

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B60.2%
    Source

    Not directly comparable

Multimodal

  • MMMU

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B83.9%
    Source

    Not directly comparable

  • MMVU

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B74.7%
    Source

    Not directly comparable

  • MathVision

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B86.2%
    Source

    Not directly comparable

  • CharXiv

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B77.2%
    Source

    Not directly comparable

  • V*

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B93.2%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B86.7%
    Source

    Not directly comparable

  • SuperGPQA

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B67.1%
    Source

    Not directly comparable

  • GPQA

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B86.6%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B82.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    MiMo-V2-Omni—
    Qwen3.5-122B-A10B93.4%
    Source

    Not directly comparable

16 public results · 1 shared

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Last updated September 27, 2026