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BenchLM
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Mercury 3 vs MiniMax M2.7

Updated October 19, 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

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Inception logo
Mercury 3

Inception

—

Evidence status unavailable

90% interval unavailable

Model B
MiniMax logo
MiniMax M2.7

MiniMax

48.19/100

Supported · Public rank #107

90% interval 36.8–59.5

Shared results
2
Mercury 3 only
6
MiniMax M2.7 only
27
Like-for-like categories
0 / 8
Supported: MiniMax M2.7How 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

    Mercury 3

    Mercury 3 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Mercury 3

    Mercury 3 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 3

    Mercury 3 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 3 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

    Mercury 3 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. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

—Mercury 334.4MiniMax M2.7

Not comparable · BenchAlign v5.8

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 v5.8 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.

Agentic

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
32.4
Supported · #92/130
Basis
BenchAlign v5.8 lane · 3 vs 8 public rows
Reading
Not comparable

Coding

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
34.4
Supported · #81/151
Basis
BenchAlign v5.8 lane · 1 vs 12 public rows
Reading
Not comparable

Reasoning

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
78.8
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
46.1
Supported · #95/186
Basis
BenchAlign v5.8 lane · 1 vs 5 public rows
Reading
Not comparable

Multilingual

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
93.0
#13/148
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Mercury 3
Not ranked
MiniMax M2.7
70.5
Unranked · 4 rankable rows
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 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

Mercury 3
$0.00028
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

Mercury 3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Mercury 3
$0.00605
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

Mercury 3 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 3
$0.0075
Fits in one request
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

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.

Documented inputs

Mercury 3

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Mercury 3

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Mercury 3

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Mercury 3

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Mercury 3

Proprietary

MiniMax M2.7

Open Weight

License

Mercury 3

Proprietary

MiniMax M2.7

Open Weight

Release date

Mercury 3

2026-10-19

MiniMax M2.7

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
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: $0.00605 vs $0.0186. Cache-heavy agent loop: $0.0075 vs $0.078.
Context tradeoff
Mercury 3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Mercury 3 or MiniMax M2.7?

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, Mercury 3 or MiniMax M2.7?

Mercury 3 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Mercury 3 or MiniMax M2.7?

Mercury 3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Mercury 3 or MiniMax M2.7?

For the stated presets, chat costs $0.00028 on Mercury 3 and $0.0009 on MiniMax M2.7; repository review costs $0.00605 and $0.0186; the cache-heavy agent loop costs $0.0075 and $0.078. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Mercury 3 or MiniMax M2.7?

Mercury 3 has the larger documented context window: 1M, compared with 200K.

Benchmark evidence

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

Browse raw public benchmark evidence35 rows

Agentic

  • τ³-bench results

    Mercury 330.2%
    Source
    MiniMax M2.767.6%
    Source

    MiniMax M2.7 leads this result

  • BrowseComp+

    Mercury 355.18%
    Source
    MiniMax M2.7—

    Not directly comparable

  • DeepSearchQA

    Mercury 371.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Mercury 3—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Mercury 3—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Mercury 3—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Mercury 3—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Mercury 3—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Mercury 3—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Mercury 3—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • DeepSWE

    Mercury 346.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Mercury 3—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Mercury 3—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Mercury 3—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Mercury 3—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Mercury 3—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Mercury 3—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Mercury 3—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Mercury 3—
    MiniMax M2.757.0%
    Source

    Not directly comparable

  • Vibe Code Bench

    Mercury 3—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Mercury 3—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Mercury 3—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Mercury 3—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Mercury 375.5%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • GPQA-D

    Mercury 389.2%
    Source
    MiniMax M2.787.0%
    Source

    Mercury 3 leads this result

  • HLE

    Mercury 3—
    MiniMax M2.728%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Mercury 3—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Mercury 3—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Mercury 3—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Mercury 376.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MultiChallenge (provider reports)

    Mercury 360.35%
    Source
    MiniMax M2.7—

    Not directly comparable

Math

  • AIME25 (Arcee)

    Mercury 3—
    MiniMax M2.780.0%
    Source

    Not directly comparable

  • AIME26

    Mercury 3—
    MiniMax M2.789.8%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Mercury 3—
    MiniMax M2.781.0%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Mercury 3—
    MiniMax M2.772.7%
    Source

    Not directly comparable

35 public results · 2 shared

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Last updated October 19, 2026