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Model comparison

MiMo-V2-Omni vs ZAYA1-74B-Preview

Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiMo-V2-Omni

Xiaomi

62.1/100

Supported · Public rank #44

90% interval 52.4–71.9

ZAYA1-74B-Preview

Zyphra

Evidence status unavailable

90% interval unavailable

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 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    MiMo-V2-Omni

    MiMo-V2-Omni leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    MiMo-V2-Omni

    MiMo-V2-Omni has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • 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

    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.

Shared results
1
MiMo-V2-Omni only
1
ZAYA1-74B-Preview only
6
Like-for-like categories
1 / 8

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.

Coding

Like-for-like
MiMo-V2-Omni
74.8
ZAYA1-74B-Preview
53.2
Weighted basis
1 vs 1 rows
Reading
MiMo-V2-Omni leads

Agentic

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
66.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
76.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
MiMo-V2-Omni
Not measured
ZAYA1-74B-Preview
Not measured
Weighted basis
0 vs 0 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

MiMo-V2-Omni
API rate not published
Fits in one request
ZAYA1-74B-Preview
Self-hosted; infrastructure cost varies
Fits in one request

MiMo-V2-Omni has no comparable published API token rate. ZAYA1-74B-Preview 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
ZAYA1-74B-Preview
Self-hosted; infrastructure cost varies
Fits in one request

MiMo-V2-Omni has no comparable published API token rate. ZAYA1-74B-Preview 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
ZAYA1-74B-Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

MiMo-V2-Omni has no comparable published API token rate. ZAYA1-74B-Preview 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.

MiMo-V2-Omni

262K

ZAYA1-74B-Preview

256K

API model ID

MiMo-V2-Omni

Not sourced

ZAYA1-74B-Preview

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

ZAYA1-74B-Preview

No comparable hosted API rate

Documented inputs

MiMo-V2-Omni

Not sourced

ZAYA1-74B-Preview

Not sourced

Documented outputs

MiMo-V2-Omni

Not sourced

ZAYA1-74B-Preview

Not sourced

Provider availability

MiMo-V2-Omni

Not sourced

ZAYA1-74B-Preview

Not sourced

Reasoning profile

MiMo-V2-Omni

Reasoning

ZAYA1-74B-Preview

Reasoning

Weight access

MiMo-V2-Omni

Proprietary

ZAYA1-74B-Preview

Open Weight

License

MiMo-V2-Omni

Proprietary

ZAYA1-74B-Preview

Open Weight

Release date

MiMo-V2-Omni

2026-03-18

ZAYA1-74B-Preview

2026-05-07

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
MiMo-V2-Omni has the larger documented window (262K).

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 evidence8 rows

Agentic

  • Claw-Eval

    MiMo-V2-Omni45.2%
    Source
    ZAYA1-74B-Preview

    Not directly comparable

  • τ²-bench Airline

    MiMo-V2-Omni
    ZAYA1-74B-Preview56.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    MiMo-V2-Omni74.8%
    Source
    ZAYA1-74B-Preview53.2%
    Source

    MiMo-V2-Omni leads this result

  • LiveCodeBench v6

    MiMo-V2-Omni
    ZAYA1-74B-Preview65.7%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    MiMo-V2-Omni
    ZAYA1-74B-Preview68.1%
    Source

    Not directly comparable

  • GPQA

    MiMo-V2-Omni
    ZAYA1-74B-Preview57.3%
    Source

    Not directly comparable

  • GPQA-D

    MiMo-V2-Omni
    ZAYA1-74B-Preview57.3%
    Source

    Not directly comparable

Math

  • AIME26

    MiMo-V2-Omni
    ZAYA1-74B-Preview76.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, MiMo-V2-Omni or ZAYA1-74B-Preview?

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, MiMo-V2-Omni or ZAYA1-74B-Preview?

MiMo-V2-Omni leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, MiMo-V2-Omni or ZAYA1-74B-Preview?

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, MiMo-V2-Omni or ZAYA1-74B-Preview?

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 ZAYA1-74B-Preview?

MiMo-V2-Omni has the larger documented context window: 262K, compared with 256K.

Related comparisons

Last updated August 7, 2026

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