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BenchLM
Inception logo
Model A
Mercury 2.5

Inception

—

Evidence status unavailable

90% interval unavailable

Mercury 2.5 vs MiniMax M3

Updated September 8, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M3

MiniMax

61.5/100

Supported · Public rank #56

90% interval 52.4–70.6

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.

  • 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

    Mercury 2.5

    Mercury 2.5 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

    Mercury 2.5

    Mercury 2.5 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

    Mercury 2.5

    Mercury 2.5 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

    Mercury 2.5 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

    Mercury 2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

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
Mercury 2.5 only
5
MiniMax M3 only
27
Like-for-like categories
0 / 8

4 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
Mercury 2.5
44.5
Estimated · #91/152
MiniMax M3
42.0
Supported · #109/152
Basis
BenchAlign lane · 2 vs 9 public rows
Reading
Directional only

Coding

Directional only
Mercury 2.5
47.8
Estimated · #73/151
MiniMax M3
48.9
Estimated · #66/151
Basis
BenchAlign lane · 1 vs 10 public rows
Reading
Directional only

Knowledge

Directional only
Mercury 2.5
48.3
Estimated · #94/182
MiniMax M3
53.4
Supported · #64/182
Basis
BenchAlign lane · 1 vs 2 public rows
Reading
Directional only

Instruction following

Directional only
Mercury 2.5
80.1
#53/121
MiniMax M3
93.7
#4/121
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Mercury 2.5
67.7
Unranked · 1 rankable row
MiniMax M3
78.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Mercury 2.5
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Mercury 2.5
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Mercury 2.5
Not ranked
MiniMax M3
52.2
#34/48
Basis
Provisional lane · 0 vs 2 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

Mercury 2.5
$0.00012
Fits in one request
MiniMax M3
$0.0009
Fits in one request

Mercury 2.5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Mercury 2.5
$0.00245
Fits in one request
MiniMax M3
$0.0186
Fits in one request

Mercury 2.5 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Mercury 2.5
$0.0031
Fits in one request
MiniMax M3
$0.03
Fits in one request

Mercury 2.5 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.

Cached-input rate

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

Mercury 2.5

$0.004 per 1M cached input tokens

Inception models and Mercury 2.5 launch pricing

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

Mercury 2.5

Not sourced

MiniMax M3

Not sourced

Documented outputs

Mercury 2.5

Not sourced

MiniMax M3

Not sourced

Provider availability

Mercury 2.5

Not sourced

MiniMax M3

Not sourced

Reasoning profile

Mercury 2.5

Reasoning

MiniMax M3

Non-Reasoning

Weight access

Mercury 2.5

Proprietary

MiniMax M3

Open Weight

License

Mercury 2.5

Proprietary

MiniMax M3

Open Weight

Release date

Mercury 2.5

2026-09-08

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.00245 vs $0.0186. Cache-heavy agent loop: $0.0031 vs $0.03.
Context tradeoff
MiniMax M3 has the larger documented window (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 evidence32 rows

Agentic

  • τ³-bench results

    Mercury 2.596.0%
    Source
    MiniMax M3—

    Not directly comparable

  • DeepSearchQA

    Mercury 2.534.0%
    Source
    MiniMax M3—

    Not directly comparable

  • Terminal-Bench 2.0

    Mercury 2.5—
    MiniMax M366%
    Source

    Not directly comparable

  • BrowseComp

    Mercury 2.5—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    Mercury 2.5—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    Mercury 2.5—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    Mercury 2.5—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    Mercury 2.5—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    Mercury 2.5—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    Mercury 2.5—
    MiniMax M34.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Mercury 2.5—
    MiniMax M353.6%
    Source

    Not directly comparable

Coding

  • SciCode

    Mercury 2.538%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    Mercury 2.5—
    MiniMax M380.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    Mercury 2.5—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Mercury 2.5—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    Mercury 2.5—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    Mercury 2.5—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    Mercury 2.5—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    Mercury 2.5—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Mercury 2.5—
    MiniMax M348.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Mercury 2.5—
    MiniMax M382.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Mercury 2.5—
    MiniMax M375.0%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Mercury 2.579.0%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA Diamond (Vals)

    Mercury 2.5—
    MiniMax M392.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Mercury 2.5—
    MiniMax M384.2%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Mercury 2.5—
    MiniMax M385.7%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Mercury 2.5—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Mercury 2.5—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    Mercury 2.5—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    Mercury 2.5—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Mercury 2.5—
    MiniMax M385.4%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Mercury 2.577%
    Source
    MiniMax M3—

    Not directly comparable

Frequently asked questions

Which is better, Mercury 2.5 or MiniMax M3?

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, Mercury 2.5 or MiniMax M3?

MiniMax M3 scores higher for coding on the public lane, 48.9 to 47.8. Mercury 2.5 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, Mercury 2.5 or MiniMax M3?

Mercury 2.5 scores higher for agentic tasks on the public lane, 44.5 to 42. Mercury 2.5 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, Mercury 2.5 or MiniMax M3?

For the stated presets, chat costs $0.00012 on Mercury 2.5 and $0.0009 on MiniMax M3; repository review costs $0.00245 and $0.0186; the cache-heavy agent loop costs $0.0031 and $0.03. Costs use the listed standard API rates.

Which has the larger context window, Mercury 2.5 or MiniMax M3?

MiniMax M3 has the larger documented context window: 1M, compared with 260K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 8, 2026

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