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InclusionAI logo
Model A
Ling 2.6 Flash

InclusionAI

43.68/100

Estimated · Public rank #176

90% interval 32.255.2

Ling 2.6 Flash 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 50.371.0

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.

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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.

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

    Ling 2.6 Flash 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

  • 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

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
0
Ling 2.6 Flash only
0
MiMo-V2-Omni only
2
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
Ling 2.6 Flash
42.7
Estimated · #127/183
MiMo-V2-Omni
54.7
Estimated · #46/183
Basis
BenchAlign lane · 0 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Ling 2.6 Flash
41.4
Estimated · #132/181
MiMo-V2-Omni
51.2
Estimated · #84/181
Basis
BenchAlign lane · 0 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
Ling 2.6 Flash
46.6
#86/120
MiMo-V2-Omni
63.9
#68/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
Ling 2.6 Flash
39.9
Estimated · #122/151
MiMo-V2-Omni
Not ranked
Basis
BenchAlign lane · 0 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Ling 2.6 Flash
41.2
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
Ling 2.6 Flash
Not ranked
MiMo-V2-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Ling 2.6 Flash
Not ranked
MiMo-V2-Omni
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 2.6 Flash
Not ranked
MiMo-V2-Omni
62.7
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

Ling 2.6 Flash
API rate not published
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

Ling 2.6 Flash 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

Ling 2.6 Flash
API rate not published
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

Ling 2.6 Flash 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

Ling 2.6 Flash
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

Ling 2.6 Flash 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.

Ling 2.6 Flash

262K

MiMo-V2-Omni

262K

API model ID

Ling 2.6 Flash

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.

Ling 2.6 Flash

No comparable hosted API rate

MiMo-V2-Omni

No comparable hosted API rate

Documented inputs

Ling 2.6 Flash

Not sourced

MiMo-V2-Omni

Not sourced

Documented outputs

Ling 2.6 Flash

Not sourced

MiMo-V2-Omni

Not sourced

Provider availability

Ling 2.6 Flash

Not sourced

MiMo-V2-Omni

Not sourced

Reasoning profile

Ling 2.6 Flash

Non-Reasoning

MiMo-V2-Omni

Reasoning

Weight access

Ling 2.6 Flash

Open Weight

MiMo-V2-Omni

Proprietary

License

Ling 2.6 Flash

Open Weight

MiMo-V2-Omni

Proprietary

Release date

Ling 2.6 Flash

2026-04-21

MiMo-V2-Omni

2026-03-18

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
Both models list 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 evidence2 rows

Agentic

  • Claw-Eval

    Ling 2.6 Flash
    MiMo-V2-Omni45.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Ling 2.6 Flash
    MiMo-V2-Omni74.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Ling 2.6 Flash or MiMo-V2-Omni?

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, Ling 2.6 Flash or MiMo-V2-Omni?

MiMo-V2-Omni scores higher for coding on the public lane, 54.7 to 42.7. Ling 2.6 Flash 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, Ling 2.6 Flash 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, Ling 2.6 Flash 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, Ling 2.6 Flash or MiMo-V2-Omni?

Both models list the same context window, 262K.

Related comparisons

Last updated September 4, 2026

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