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BenchLM

MiniMax M3 vs Toast 1

Updated September 28, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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.

Model A
MiniMax logo
MiniMax M3

MiniMax

54.78/100

Supported · Public rank #59

90% interval 46.2–63.4

Model B
Toast 1

Mixedbread

—

Evidence status unavailable

90% interval unavailable

Shared results
0
MiniMax M3 only
27
Toast 1 only
0
Like-for-like categories
0 / 8
Supported: MiniMax M3How 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.

  • Long documents

    Prompts that approach the documented context limit

    MiniMax M3

    MiniMax M3 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Toast 1

    Toast 1 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

    Toast 1

    Toast 1 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

    Not enough matched evidence

    Toast 1 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Toast 1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Toast 1 does not fit this workload in one request.

    Confidence: listed-rates

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.

38.7MiniMax M3—Toast 1

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Agentic

Not comparable
MiniMax M3
39.7
Supported · #52/111
Toast 1
Not ranked
Basis
BenchAlign v5.7 lane · 9 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
MiniMax M3
38.7
Supported · #59/136
Toast 1
Not ranked
Basis
BenchAlign v5.7 lane · 10 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
MiniMax M3
79.4
#5/27
Toast 1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
MiniMax M3
52.0
#37/50
Toast 1
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
MiniMax M3
48.3
Supported · #63/160
Toast 1
Not ranked
Basis
BenchAlign v5.7 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
MiniMax M3
Not ranked
Toast 1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
MiniMax M3
92.4
#5/124
Toast 1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
MiniMax M3
Not ranked
Toast 1
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 v5.7) 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

MiniMax M3
$0.0009
Fits in one request
Toast 1
$0.00066
Fits in one request

Toast 1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

MiniMax M3
$0.0186
Fits in one request
Toast 1
$0.01716
Fits in one request

Toast 1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

MiniMax M3
$0.03
Fits in one request
Toast 1
$0.0204
Does not fit in one request

Toast 1 does not fit this workload in one request.

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.

MiniMax M3

$0.06 per 1M cached input tokens

Toast 1

$0.036 per 1M cached input tokens

Mixedbread pricing

Reasoning profile

MiniMax M3

Non-Reasoning

Toast 1

Reasoning

Weight access

MiniMax M3

Open Weight

Toast 1

Proprietary

License

MiniMax M3

Open Weight

Toast 1

Proprietary

Release date

MiniMax M3

2026-06-01

Toast 1

2026-08-13

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
Repository review: $0.0186 vs $0.01716. Cache-heavy agent loop: $0.03 vs $0.0204.
Context tradeoff
MiniMax M3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, MiniMax M3 or Toast 1?

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, MiniMax M3 or Toast 1?

Toast 1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, MiniMax M3 or Toast 1?

Toast 1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, MiniMax M3 or Toast 1?

For the stated presets, chat costs $0.0009 on MiniMax M3 and $0.00066 on Toast 1; repository review costs $0.0186 and $0.01716; the cache-heavy agent loop costs $0.03 and $0.0204. Toast 1 does not fit this workload in one request.

Which has the larger context window, MiniMax M3 or Toast 1?

MiniMax M3 has the larger documented context window: 1M, compared with 131K.

Benchmark evidence

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

Browse raw public benchmark evidence27 rows

Agentic

  • Terminal-Bench 2.1

    MiniMax M366.0%
    Source
    Toast 1—

    Not directly comparable

  • BrowseComp

    MiniMax M383.5%
    Source
    Toast 1—

    Not directly comparable

  • OSWorld-Verified

    MiniMax M370.1%
    Source
    Toast 1—

    Not directly comparable

  • MCP Atlas

    MiniMax M374.2%
    Source
    Toast 1—

    Not directly comparable

  • Claw-Eval

    MiniMax M374.5%
    Source
    Toast 1—

    Not directly comparable

  • BankerToolBench

    MiniMax M376.1%
    Source
    Toast 1—

    Not directly comparable

  • ResearchClawBench

    MiniMax M319.8%
    Source
    Toast 1—

    Not directly comparable

  • OSWorld 2.0

    MiniMax M34.6%
    Source
    Toast 1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    MiniMax M353.6%
    Source
    Toast 1—

    Not directly comparable

Coding

  • SWE-bench Verified

    MiniMax M380.5%
    Source
    Toast 1—

    Not directly comparable

  • SWE-bench Pro

    MiniMax M359%
    Source
    Toast 1—

    Not directly comparable

  • Terminal-Bench 2.1

    MiniMax M366.0%
    Source
    Toast 1—

    Not directly comparable

  • NL2Repo

    MiniMax M342.1%
    Source
    Toast 1—

    Not directly comparable

  • VIBE V2

    MiniMax M350.1%
    Source
    Toast 1—

    Not directly comparable

  • SVG-Bench

    MiniMax M363.7%
    Source
    Toast 1—

    Not directly comparable

  • KernelBench Hard

    MiniMax M328.8%
    Source
    Toast 1—

    Not directly comparable

  • OpenHarmony Bench

    MiniMax M348.4%
    Source
    Toast 1—

    Not directly comparable

  • LiveCodeBench (Vals)

    MiniMax M382.2%
    Source
    Toast 1—

    Not directly comparable

  • SWE-bench (Vals)

    MiniMax M375.0%
    Source
    Toast 1—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    MiniMax M345.1%
    Source
    Toast 1—

    Not directly comparable

  • OmniDocBench 1.5

    MiniMax M391.6%
    Source
    Toast 1—

    Not directly comparable

  • MMMU-Pro

    MiniMax M378.1%
    Source
    Toast 1—

    Not directly comparable

  • VideoMMMU

    MiniMax M384.6%
    Source
    Toast 1—

    Not directly comparable

  • Video-MME (with subtitle)

    MiniMax M385.4%
    Source
    Toast 1—

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    MiniMax M392.7%
    Source
    Toast 1—

    Not directly comparable

  • MMLU-Pro (Vals)

    MiniMax M384.2%
    Source
    Toast 1—

    Not directly comparable

Math

  • USAMO 2026

    MiniMax M385.7%
    Source
    Toast 1—

    Not directly comparable

27 public results · 0 shared

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Last updated September 28, 2026