Skip to main content
BenchLM
Advertise

Model comparison

GLM-5 (Reasoning) vs MiniMax M2.7

Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

20 confirmed releases in the last 30 daysSee provider release alerts
GLM-5 (Reasoning)

Z.AI

59.0/100

Estimated · Public rank #59

90% interval 47.5–70.5

MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #40

90% interval 56.5–69.7

MiniMax M2.7 has the higher public score estimate, 63.09 versus 58.99, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    MiniMax M2.7

    MiniMax M2.7 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

    MiniMax M2.7

    MiniMax M2.7 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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. GLM-5 (Reasoning) does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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
1
GLM-5 (Reasoning) only
0
MiniMax M2.7 only
17
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
57.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
53.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5 (Reasoning)
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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

GLM-5 (Reasoning)
$0.0026
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5 (Reasoning)
$0.0596
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GLM-5 (Reasoning)
$0.252
Does not fit in one request
Cached input priced at the published list-input rate
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

GLM-5 (Reasoning) does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

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.

GLM-5 (Reasoning)

200K

MiniMax M2.7

200K

API model ID

GLM-5 (Reasoning)

Not sourced

MiniMax M2.7

Not sourced

Cached-input rate

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

GLM-5 (Reasoning)

Not published

MiniMax M2.7

Not published

Documented inputs

GLM-5 (Reasoning)

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GLM-5 (Reasoning)

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GLM-5 (Reasoning)

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GLM-5 (Reasoning)

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GLM-5 (Reasoning)

Open Weight

MiniMax M2.7

Open Weight

License

GLM-5 (Reasoning)

Open Weight

MiniMax M2.7

Open Weight

Release date

GLM-5 (Reasoning)

2026-03-01

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
MiniMax M2.7 has the higher public score estimate, 63.09 versus 58.99, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0596 vs $0.0186. Cache-heavy agent loop: $0.252 vs $0.078.
Context tradeoff
Both models list 200K.

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 evidence18 rows

Agentic

  • Terminal-Bench 2.0

    GLM-5 (Reasoning)—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    GLM-5 (Reasoning)—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GLM-5 (Reasoning)—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GLM-5 (Reasoning)—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-5 (Reasoning)—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5 (Reasoning)—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    GLM-5 (Reasoning)23.36%
    MiniMax M2.727.04%

    MiniMax M2.7 leads this result

  • SWE-bench Verified*

    GLM-5 (Reasoning)—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GLM-5 (Reasoning)—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    GLM-5 (Reasoning)—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GLM-5 (Reasoning)—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GLM-5 (Reasoning)—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GLM-5 (Reasoning)—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GLM-5 (Reasoning)—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • React Native Evals

    GLM-5 (Reasoning)—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GLM-5 (Reasoning)—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GLM-5 (Reasoning)—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    GLM-5 (Reasoning)—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-5 (Reasoning) or MiniMax M2.7?

MiniMax M2.7 has the higher public score estimate, 63.09 versus 58.99, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5 (Reasoning) or MiniMax M2.7?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GLM-5 (Reasoning) or MiniMax M2.7?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GLM-5 (Reasoning) or MiniMax M2.7?

For the stated presets, chat costs $0.0026 on GLM-5 (Reasoning) and $0.0009 on MiniMax M2.7; repository review costs $0.0596 and $0.0186; the cache-heavy agent loop costs $0.252 and $0.078. GLM-5 (Reasoning) does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5 (Reasoning) or MiniMax M2.7?

Both models list the same context window, 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 1, 2026

Watch GLM-5 (Reasoning) vs MiniMax M2.7

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.