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BenchLM
Poolside logo
Model A
Laguna S 2.1

Poolside

—

Evidence status unavailable

90% interval unavailable

Laguna S 2.1 vs MiniMax M3

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

MiniMax logo
Model B
MiniMax M3

MiniMax

63.91/100

Supported · Public rank #51

90% interval 56.5–71.3

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

3 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Laguna S 2.1

    Laguna S 2.1 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

    Laguna S 2.1

    Laguna S 2.1 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

    Laguna S 2.1

    Laguna S 2.1 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

    Laguna S 2.1 and MiniMax M3 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

    Laguna S 2.1 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

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
3
Laguna S 2.1 only
3
MiniMax M3 only
24
Like-for-like categories
0 / 8

2 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.

Agentic

Directional only
Laguna S 2.1
48.2
Estimated · #77/151
MiniMax M3
43.2
Supported · #106/151
Basis
BenchAlign lane · 2 vs 9 public rows
Reading
Directional only

Coding

Directional only
Laguna S 2.1
47.8
Estimated · #87/183
MiniMax M3
50.6
Estimated · #68/183
Basis
BenchAlign lane · 4 vs 10 public rows
Reading
Directional only

Reasoning

Not comparable
Laguna S 2.1
Not ranked
MiniMax M3
78.5
#5/22
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Laguna S 2.1
Not ranked
MiniMax M3
53.8
Supported · #64/181
Basis
BenchAlign lane · 0 vs 2 public rows
Reading
Not comparable

Math

Not comparable
Laguna S 2.1
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Laguna S 2.1
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Laguna S 2.1
Not ranked
MiniMax M3
51.5
#34/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Laguna S 2.1
Not ranked
MiniMax M3
93.5
#4/120
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.

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

Laguna S 2.1
$0.0002
Fits in one request
MiniMax M3
$0.0009
Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Laguna S 2.1
$0.0056
Fits in one request
MiniMax M3
$0.0186
Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Laguna S 2.1
$0.006
Fits in one request
MiniMax M3
$0.03
Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

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.

Laguna S 2.1

1M

MiniMax M3

1M

API model ID

Laguna S 2.1

Not sourced

MiniMax M3

Not sourced

Cached-input rate

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

Laguna S 2.1

$0.01 per 1M cached input tokens

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

Laguna S 2.1

Not sourced

MiniMax M3

Not sourced

Documented outputs

Laguna S 2.1

Not sourced

MiniMax M3

Not sourced

Provider availability

Laguna S 2.1

Not sourced

MiniMax M3

Not sourced

Reasoning profile

Laguna S 2.1

Reasoning

MiniMax M3

Non-Reasoning

Weight access

Laguna S 2.1

Open Weight

MiniMax M3

Open Weight

License

Laguna S 2.1

Open Weight

MiniMax M3

Open Weight

Release date

Laguna S 2.1

2026-07-21

MiniMax M3

2026-06-01

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.0056 vs $0.0186. Cache-heavy agent loop: $0.006 vs $0.03.
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 evidence30 rows

Agentic

  • Terminal-Bench 2.0

    Laguna S 2.170.2%
    Source
    MiniMax M366%
    Source

    Laguna S 2.1 leads this result

  • Toolathlon-Verified

    Laguna S 2.149.7%
    Source
    MiniMax M3—

    Not directly comparable

  • BrowseComp

    Laguna S 2.1—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    Laguna S 2.1—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    Laguna S 2.1—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    Laguna S 2.1—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    Laguna S 2.1—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    Laguna S 2.1—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    Laguna S 2.1—
    MiniMax M34.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Laguna S 2.1—
    MiniMax M353.6%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Laguna S 2.170.2%
    Source
    MiniMax M366.0%
    Source

    Laguna S 2.1 leads this result

  • SWE Multilingual

    Laguna S 2.178.5%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Pro

    Laguna S 2.159.4%
    Source
    MiniMax M359%
    Source

    Laguna S 2.1 leads this result

  • deepSwe

    Laguna S 2.140.4%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    Laguna S 2.1—
    MiniMax M380.5%
    Source

    Not directly comparable

  • NL2Repo

    Laguna S 2.1—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    Laguna S 2.1—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    Laguna S 2.1—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    Laguna S 2.1—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Laguna S 2.1—
    MiniMax M348.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Laguna S 2.1—
    MiniMax M382.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Laguna S 2.1—
    MiniMax M375.0%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Laguna S 2.1—
    MiniMax M392.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Laguna S 2.1—
    MiniMax M384.2%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Laguna S 2.1—
    MiniMax M385.7%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Laguna S 2.1—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Laguna S 2.1—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    Laguna S 2.1—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    Laguna S 2.1—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Laguna S 2.1—
    MiniMax M385.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Laguna S 2.1 or MiniMax M3?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Laguna S 2.1 or MiniMax M3?

MiniMax M3 scores higher for coding on the public lane, 50.6 to 47.8. Laguna S 2.1 and MiniMax M3 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, Laguna S 2.1 or MiniMax M3?

Laguna S 2.1 scores higher for agentic tasks on the public lane, 48.2 to 43.2. Laguna S 2.1 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, Laguna S 2.1 or MiniMax M3?

For the stated presets, chat costs $0.0002 on Laguna S 2.1 and $0.0009 on MiniMax M3; repository review costs $0.0056 and $0.0186; the cache-heavy agent loop costs $0.006 and $0.03. Costs use the listed standard API rates.

Which has the larger context window, Laguna S 2.1 or MiniMax M3?

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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