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Radar

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BenchLM
Xiaomi logo
Model A
MiMo-V2.5-Pro

Xiaomi

65.41/100

Supported · Public rank #41

90% interval 56.8–74.0

MiMo-V2.5-Pro vs MiMo-V2-Omni

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

Xiaomi logo
Model B
MiMo-V2-Omni

Xiaomi

60.62/100

Supported · Public rank #70

90% interval 50.3–71.0

Decision reading

MiMo-V2.5-Pro has the higher public score estimate, 65.41 versus 60.62, 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.

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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-Pro

    MiMo-V2.5-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

    MiMo-V2.5-Pro and MiMo-V2-Omni are 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-Omni is not ranked on the public lane for agentic, so no winner is named for agentic.

    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-V2.5-Pro only
12
MiMo-V2-Omni only
1
Like-for-like categories
0 / 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.

Coding

Directional only
MiMo-V2.5-Pro
57.1
Estimated · #36/183
MiMo-V2-Omni
54.7
Estimated · #47/183
Basis
BenchAlign lane · 4 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
MiMo-V2.5-Pro
55.5
Supported · #58/181
MiMo-V2-Omni
51.2
Estimated · #83/181
Basis
BenchAlign lane · 4 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
MiMo-V2.5-Pro
93.5
#6/120
MiMo-V2-Omni
63.9
#68/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
MiMo-V2.5-Pro
40.8
Supported · #118/151
MiMo-V2-Omni
Not ranked
Basis
BenchAlign lane · 5 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
MiMo-V2.5-Pro
76.9
Unranked · 2 rankable rows
MiMo-V2-Omni
72.6
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
MiMo-V2.5-Pro
Not ranked
MiMo-V2-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
MiMo-V2.5-Pro
Not ranked
MiMo-V2-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
MiMo-V2.5-Pro
Not ranked
MiMo-V2-Omni
63.0
Unranked · 1 rankable row
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.

A shared-evidence shape is not available.

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

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-Pro
API rate not published
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

MiMo-V2.5-Pro has no comparable published API token rate. MiMo-V2-Omni has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiMo-V2.5-Pro
API rate not published
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

MiMo-V2.5-Pro has no comparable published API token rate. MiMo-V2-Omni has no comparable published API token rate.

Cache-heavy agent loop

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

MiMo-V2.5-Pro
API rate not published
Fits in one request
Cached-input rate unavailable
MiMo-V2-Omni
API rate not published
Fits in one request
Cached-input rate unavailable

MiMo-V2.5-Pro has no comparable published API token rate. MiMo-V2-Omni 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-V2.5-Pro

1M

MiMo-V2-Omni

262K

API model ID

MiMo-V2.5-Pro

Not sourced

MiMo-V2-Omni

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-Pro

No comparable hosted API rate

MiMo-V2-Omni

No comparable hosted API rate

Documented inputs

MiMo-V2.5-Pro

Not sourced

MiMo-V2-Omni

Not sourced

Documented outputs

MiMo-V2.5-Pro

Not sourced

MiMo-V2-Omni

Not sourced

Provider availability

MiMo-V2.5-Pro

Not sourced

MiMo-V2-Omni

Not sourced

Reasoning profile

MiMo-V2.5-Pro

Reasoning

MiMo-V2-Omni

Reasoning

Weight access

MiMo-V2.5-Pro

Proprietary

MiMo-V2-Omni

Proprietary

License

MiMo-V2.5-Pro

Proprietary

MiMo-V2-Omni

Proprietary

Release date

MiMo-V2.5-Pro

2026-04-22

MiMo-V2-Omni

2026-03-18

If you already use one of these models
Deployment change
Both entries list Xiaomi as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
MiMo-V2.5-Pro has the higher public score estimate, 65.41 versus 60.62, 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-Pro has the larger documented window (1M).

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 evidence14 rows

Agentic

  • Claw-Eval

    Shared source
    MiMo-V2.5-Pro63.8%
    MiMo-V2-Omni45.2%

    MiMo-V2.5-Pro leads this result

  • τ³-bench results

    MiMo-V2.5-Pro72.9%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • Terminal-Bench 2.0

    MiMo-V2.5-Pro68.4%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • Gert Labs

    MiMo-V2.5-Pro62.70%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    MiMo-V2.5-Pro57.3%
    Source
    MiMo-V2-Omni—

    Not directly comparable

Coding

  • SWE-bench Pro

    MiMo-V2.5-Pro57.2%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • Terminal-Bench 2.0

    MiMo-V2.5-Pro68.4%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • LiveCodeBench (Vals)

    MiMo-V2.5-Pro81.4%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • SWE-bench (Vals)

    MiMo-V2.5-Pro74.0%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • SWE-bench Verified

    MiMo-V2.5-Pro—
    MiMo-V2-Omni74.8%
    Source

    Not directly comparable

Knowledge

  • HLE

    MiMo-V2.5-Pro48%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • HLE w/o tools

    MiMo-V2.5-Pro34%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • GPQA Diamond (Vals)

    MiMo-V2.5-Pro82.6%
    Source
    MiMo-V2-Omni—

    Not directly comparable

  • MMLU-Pro (Vals)

    MiMo-V2.5-Pro84.6%
    Source
    MiMo-V2-Omni—

    Not directly comparable

Frequently asked questions

Which is better, MiMo-V2.5-Pro or MiMo-V2-Omni?

MiMo-V2.5-Pro has the higher public score estimate, 65.41 versus 60.62, 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-Pro or MiMo-V2-Omni?

MiMo-V2.5-Pro scores higher for coding on the public lane, 57.1 to 54.7. MiMo-V2.5-Pro and MiMo-V2-Omni are 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-Pro or MiMo-V2-Omni?

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-V2.5-Pro or MiMo-V2-Omni?

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-Pro or MiMo-V2-Omni?

MiMo-V2.5-Pro 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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