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BenchLM
Anthropic logo
Model A
Claude Fable 5

Anthropic

80.9/100

Supported · Public rank #3

90% interval 78.7–83.1

Claude Fable 5 vs MiMo-V2-Pro

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-Pro

Xiaomi

64.89/100

Supported · Public rank #46

90% interval 56.5–73.3

Decision reading

Claude Fable 5 has the higher public score, 80.9 versus 64.89, and the 90% score intervals do not overlap.

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.

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-Pro 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-Pro 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
1
Claude Fable 5 only
17
MiMo-V2-Pro only
3
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
Claude Fable 5
76.9
Supported · #2/183
MiMo-V2-Pro
57.0
Estimated · #38/183
Basis
BenchAlign lane · 10 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Claude Fable 5
83.5
Supported · #2/181
MiMo-V2-Pro
55.2
Estimated · #61/181
Basis
BenchAlign lane · 2 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
Claude Fable 5
78.3
#54/120
MiMo-V2-Pro
83.7
#48/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
Claude Fable 5
74.8
Supported · #3/151
MiMo-V2-Pro
Not ranked
Basis
BenchAlign lane · 4 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Fable 5
76.2
#11/22
MiMo-V2-Pro
68.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

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

Multilingual

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

Multimodal

Not comparable
Claude Fable 5
62.5
Unranked · 2 rankable rows
MiMo-V2-Pro
Not ranked
Basis
Provisional lane · 1 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.

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

Claude Fable 5
$0.035
Fits in one request
MiMo-V2-Pro
API rate not published
Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
MiMo-V2-Pro
API rate not published
Fits in one request

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

Cache-heavy agent loop

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

Claude Fable 5
$0.9
Fits in one request
MiMo-V2-Pro
API rate not published
Fits in one request
Cached-input rate unavailable

MiMo-V2-Pro 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.

Claude Fable 5

1M

Anthropic model overview

MiMo-V2-Pro

1M

Cached-input rate

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

Claude Fable 5

$1 per 1M cached input tokens

Claude API pricing

MiMo-V2-Pro

No comparable hosted API rate

Reasoning profile

Claude Fable 5

Reasoning

MiMo-V2-Pro

Reasoning

Weight access

Claude Fable 5

Proprietary

MiMo-V2-Pro

Proprietary

License

Claude Fable 5

Proprietary

MiMo-V2-Pro

Proprietary

Release date

Claude Fable 5

2026-06-09

MiMo-V2-Pro

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
Claude Fable 5 has the higher public score, 80.9 versus 64.89, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 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 evidence21 rows

Agentic

  • Terminal-Bench 3.0

    Claude Fable 534.0%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • OSWorld-Verified

    Claude Fable 585%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Fable 580.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • Claw-Eval

    Claude Fable 5—
    MiMo-V2-Pro57.8%
    Source

    Not directly comparable

  • Gert Labs

    Claude Fable 5—
    MiMo-V2-Pro36.68%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Fable 5—
    MiMo-V2-Pro15.3%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    MiMo-V2-Pro78%
    Source

    Claude Fable 5 leads this result

  • SWE-bench Pro

    Claude Fable 580%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • FrontierSWE v2

    Claude Fable 548.0%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • cursorBench31

    Claude Fable 570.6%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • cursorBench32

    Claude Fable 570.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • VulcanBench v3

    Claude Fable 589.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Fable 589.8%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Fable 595.0%
    Source
    MiMo-V2-Pro—

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Fable 593.2%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Fable 591.5%
    Source
    MiMo-V2-Pro—

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    MiMo-V2-Pro—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    MiMo-V2-Pro—

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or MiMo-V2-Pro?

Claude Fable 5 has the higher public score, 80.9 versus 64.89, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Fable 5 or MiMo-V2-Pro?

Claude Fable 5 scores higher for coding on the public lane, 76.9 to 57. MiMo-V2-Pro 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, Claude Fable 5 or MiMo-V2-Pro?

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

Which costs less, Claude Fable 5 or MiMo-V2-Pro?

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, Claude Fable 5 or MiMo-V2-Pro?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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