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Claude Sonnet 5 vs MiMo-V2.6-Pro

Updated November 5, 2026. Rank says Claude Sonnet 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Sonnet 5 has the higher public point estimate, 64.45 versus 57.27. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo
Claude Sonnet 5

Anthropic

64.45/100

Supported · Public rank #35

90% interval 59.1–69.8

Model B
Xiaomi logo
MiMo-V2.6-Pro

Xiaomi

57.27/100

Estimated · Public rank #58

Conditional range 47.5–67.0

Shared results
3
Claude Sonnet 5 only
28
MiMo-V2.6-Pro only
11
Like-for-like categories
2 / 8
Supported: Claude Sonnet 5 · Estimated: MiMo-V2.6-Pro. Conditional ranges do not establish rank confidence.How 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiMo-V2.6-Pro

    MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    MiMo-V2.6-Pro

    MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    MiMo-V2.6-Pro

    MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    No clear pick

    The like-for-like coding result is a practical tie on the public lane (within 0.5 points).

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    MiMo-V2.6-Pro is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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.

57.4Claude Sonnet 557.1MiMo-V2.6-Pro

Like-for-like · BenchAlign v5.8

Claude Sonnet 5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Open the coding leaderboardShortlist models for this job
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.

1 category rests 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 v5.8 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

Like-for-like
Claude Sonnet 5
57.4
Supported · #24/156
MiMo-V2.6-Pro
57.1
Supported · #25/156
Basis
BenchAlign v5.8 lane · 11 vs 4 public rows
Reading
Practical tie

Knowledge

Like-for-like
Claude Sonnet 5
64.6
Supported · #35/177
MiMo-V2.6-Pro
62.3
Supported · #42/177
Basis
BenchAlign v5.8 lane · 6 vs 0 public rows
Reading
Claude Sonnet 5 leads · intervals overlap

Agentic

Directional only
Claude Sonnet 5
65.0
Supported · #17/135
MiMo-V2.6-Pro
56.6
Estimated · #40/135
Basis
BenchAlign v5.8 lane · 9 vs 9 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Sonnet 5
81.3
Unranked · 2 rankable rows
MiMo-V2.6-Pro
82.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 5
80.3
#20/58
MiMo-V2.6-Pro
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 5
Not ranked
MiMo-V2.6-Pro
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 5
Not ranked
MiMo-V2.6-Pro
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 5
69.4
Unranked · 2 rankable rows
MiMo-V2.6-Pro
Not ranked
Basis
Provisional lane · 2 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 v5.8) 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

Claude Sonnet 5
$0.007
Fits in one request
MiMo-V2.6-Pro
$0.00087
Fits in one request

MiMo-V2.6-Pro has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 5
$0.13
Fits in one request
MiMo-V2.6-Pro
$0.02436
Fits in one request

MiMo-V2.6-Pro has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Sonnet 5
$0.18
Fits in one request
MiMo-V2.6-Pro
$0.01812
Fits in one request

MiMo-V2.6-Pro has the lower modeled cost

Costs use the listed standard API rates.

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.

Cached-input rate

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

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

MiMo-V2.6-Pro

$0.0036 per 1M cached input tokens

Xiaomi MiMo-V2.6 launch

Reasoning profile

Claude Sonnet 5

Reasoning

MiMo-V2.6-Pro

Reasoning

Weight access

Claude Sonnet 5

Proprietary

MiMo-V2.6-Pro

Open Weight

License

Claude Sonnet 5

Proprietary

MiMo-V2.6-Pro

Open Weight

Release date

Claude Sonnet 5

2026-06-30

MiMo-V2.6-Pro

2026-09-22

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 Sonnet 5 has the higher public point estimate, 64.45 versus 57.27. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
Repository review: $0.13 vs $0.02436. Cache-heavy agent loop: $0.18 vs $0.01812.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Sonnet 5 or MiMo-V2.6-Pro?

Claude Sonnet 5 has the higher public point estimate, 64.45 versus 57.27. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Sonnet 5 or MiMo-V2.6-Pro?

The like-for-like coding row is a practical tie on the public lane, 57.4 against 57.1, inside the 0.5-point band BenchLM treats as level.

Which is better for agentic tasks, Claude Sonnet 5 or MiMo-V2.6-Pro?

Claude Sonnet 5 scores higher for agentic tasks on the public lane, 65 to 56.6. MiMo-V2.6-Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Sonnet 5 or MiMo-V2.6-Pro?

For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00087 on MiMo-V2.6-Pro; repository review costs $0.13 and $0.02436; the cache-heavy agent loop costs $0.18 and $0.01812. Costs use the listed standard API rates.

Which has the larger context window, Claude Sonnet 5 or MiMo-V2.6-Pro?

Both models list the same context window, 1M.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Sonnet 5
API / mo$9,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
MiMo-V2.6-Pro
API / mo$979
Self-host / mo$37,843
Break-even2.2B/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence42 rows

Agentic

  • Terminal-Bench 3.0

    Claude Sonnet 514.6%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Sonnet 580.4%
    Source
    MiMo-V2.6-Pro89.9%
    Source

    MiMo-V2.6-Pro leads this result

  • BrowseComp

    Claude Sonnet 584.7%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • HLE w/ tools

    Claude Sonnet 557.4%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 581.2%
    Source
    MiMo-V2.6-Pro82%
    Source

    MiMo-V2.6-Pro leads this result

  • Terminal-Bench 2.1 (Vals)

    Claude Sonnet 574.5%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • ApprenticeBench

    Claude Sonnet 516%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • MazeBench

    Claude Sonnet 50%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • OSWorld 2.1

    Claude Sonnet 557.0%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • Toolathlon-Verified

    Claude Sonnet 5—
    MiMo-V2.6-Pro76.9%
    Source

    Not directly comparable

  • AutomationBench

    Claude Sonnet 5—
    MiMo-V2.6-Pro53.1%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Sonnet 5—
    MiMo-V2.6-Pro31.6%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Claude Sonnet 5—
    MiMo-V2.6-Pro34.90%
    Source

    Not directly comparable

  • JobBench

    Claude Sonnet 5—
    MiMo-V2.6-Pro62.0%
    Source

    Not directly comparable

  • CyberGym

    Claude Sonnet 5—
    MiMo-V2.6-Pro94.0%
    Source

    Not directly comparable

  • ExploitGym

    Claude Sonnet 5—
    MiMo-V2.6-Pro17.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 585.2%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • SWE-bench Pro

    Claude Sonnet 563.2%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • SWE Multilingual

    Claude Sonnet 578.3%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • SWE Multimodal

    Claude Sonnet 528.1%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Sonnet 580.4%
    Source
    MiMo-V2.6-Pro89.9%
    Source

    MiMo-V2.6-Pro leads this result

  • FrontierCode 1.1 Main

    Claude Sonnet 542.7%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • CursorBench 3.2

    Claude Sonnet 561.5%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • VulcanBench CII v1

    Claude Sonnet 589.2%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Sonnet 582.4%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Sonnet 579.6%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • CursorBench 4.0

    Claude Sonnet 534.1%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • DeepSWE

    Claude Sonnet 5—
    MiMo-V2.6-Pro71.9%
    Source

    Not directly comparable

  • ProgramBench

    Claude Sonnet 5—
    MiMo-V2.6-Pro26.5%
    Source

    Not directly comparable

  • Bug Hunt Bench

    Claude Sonnet 5—
    MiMo-V2.6-Pro22.7 fixes
    Source

    Not directly comparable

Reasoning

  • STEMBench

    Claude Sonnet 5—
    MiMo-V2.6-Pro2.17
    Source

    Not directly comparable

Multimodal

  • ParseBench

    Claude Sonnet 561.16%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • CharXiv

    Claude Sonnet 588.3%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • CharXiv w/o tools

    Claude Sonnet 577%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

Knowledge

  • HLE

    Claude Sonnet 557.4%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • HLE w/o tools

    Claude Sonnet 543.2%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • HLE-Verified

    Claude Sonnet 531.0%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • LABBench2

    Claude Sonnet 580.1%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Sonnet 588.9%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Sonnet 587.5%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Sonnet 565.614%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Sonnet 529.268%
    Source
    MiMo-V2.6-Pro—

    Not directly comparable

42 public results · 3 shared

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Last updated November 5, 2026