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GLM-5 vs MiniMax M2.7

Updated October 29, 2026. Rank says GLM-5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

GLM-5 has the higher public score estimate, 54.32 versus 47.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 19 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Z.AI logo
GLM-5

Z.AI

54.32/100

Supported · Public rank #75

90% interval 43.3–65.3

Model B
MiniMax logo
MiniMax M2.7

MiniMax

47.29/100

Supported · Public rank #111

90% interval 35.2–59.4

Shared results
19
GLM-5 only
19
MiniMax M2.7 only
10
Like-for-like categories
0 / 8
Supported: GLM-5 and 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.

  • 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

    GLM-5 is 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

    GLM-5 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

  • 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 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5 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

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.

38.6GLM-534.7MiniMax M2.7

Directional only · BenchAlign v5.8

GLM-5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

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

Directional only
GLM-5
43.1
Estimated · #69/135
MiniMax M2.7
33.2
Supported · #99/135
Basis
BenchAlign v5.8 lane · 13 vs 8 public rows
Reading
Directional only

Coding

Directional only
GLM-5
38.6
Estimated · #73/156
MiniMax M2.7
34.7
Supported · #85/156
Basis
BenchAlign v5.8 lane · 8 vs 12 public rows
Reading
Directional only

Knowledge

Directional only
GLM-5
49.3
Estimated · #81/179
MiniMax M2.7
45.2
Supported · #94/179
Basis
BenchAlign v5.8 lane · 6 vs 5 public rows
Reading
Directional only

Instruction following

Directional only
GLM-5
88.6
#38/157
MiniMax M2.7
93.0
#14/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
GLM-5
70.1
Unranked · 4 rankable rows
MiniMax M2.7
78.8
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5
89.5
#6/17
MiniMax M2.7
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5
83.0
Unranked · 6 rankable rows
MiniMax M2.7
70.5
Unranked · 4 rankable rows
Basis
Provisional lane · 2 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

GLM-5
$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
$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
$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 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5 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.

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.

Context window

Maximum documented context; output-token limits may be lower.

GLM-5

200K

MiniMax M2.7

200K

API model ID

GLM-5

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

Not published

MiniMax M2.7

Not published

Documented inputs

GLM-5

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GLM-5

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GLM-5

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GLM-5

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GLM-5

Open Weight

MiniMax M2.7

Open Weight

License

GLM-5

Open Weight

MiniMax M2.7

Open Weight

Release date

GLM-5

2026-02-12

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
GLM-5 has the higher public score estimate, 54.32 versus 47.29, 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.

Questions

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

GLM-5 has the higher public score estimate, 54.32 versus 47.29, 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 or MiniMax M2.7?

GLM-5 scores higher for coding on the public lane, 38.6 to 34.7. GLM-5 is 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, GLM-5 or MiniMax M2.7?

GLM-5 scores higher for agentic tasks on the public lane, 43.1 to 33.2. GLM-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, GLM-5 or MiniMax M2.7?

For the stated presets, chat costs $0.0026 on GLM-5 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 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5 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 or MiniMax M2.7?

Both models list the same context window, 200K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5
API / mo$3,150
Self-host / mo$27,962
Break-even561M/day
MiniMax M2.7
API / mo$1,125
Self-host / mo$6,990
Break-even409M/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence48 rows

Agentic

  • Terminal-Bench 2.0

    GLM-556.2%
    Source
    MiniMax M2.757%
    Source

    MiniMax M2.7 leads this result

  • BrowseComp

    GLM-562%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/ tools

    GLM-550.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Claw-Eval

    GLM-557.7%
    Source
    MiniMax M2.748.7%
    Source

    GLM-5 leads this result

  • QwenClawBench

    GLM-554.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • τ³-bench results

    GLM-565.6%
    Source
    MiniMax M2.767.6%
    Source

    MiniMax M2.7 leads this result

  • DeepPlanning

    GLM-514.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon

    GLM-538%
    Source
    MiniMax M2.746.3%
    Source

    MiniMax M2.7 leads this result

  • MCP Atlas

    GLM-531.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MCP-Tasks

    GLM-560.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • WideResearch

    GLM-569.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CyberGym

    GLM-543.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Gert Labs

    Shared source
    GLM-550.99%
    MiniMax M2.740.40%

    GLM-5 leads this result

  • MLE-Bench Lite

    GLM-5—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GLM-5—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GLM-577.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Shared source
    GLM-572.8%
    MiniMax M2.775.4%

    MiniMax M2.7 leads this result

  • SWE-bench Pro

    GLM-555.1%
    Source
    MiniMax M2.756.2%
    Source

    MiniMax M2.7 leads this result

  • SWE Multilingual

    GLM-573.3%
    Source
    MiniMax M2.776.5%
    Source

    MiniMax M2.7 leads this result

  • NL2Repo

    GLM-535.9%
    Source
    MiniMax M2.739.8%
    Source

    MiniMax M2.7 leads this result

  • SWE-Rebench

    GLM-562.8%
    Source
    MiniMax M2.751.9%
    Source

    GLM-5 leads this result

  • React Native Evals

    Shared source
    GLM-574.8%
    MiniMax M2.771.4%

    GLM-5 leads this result

  • Terminal-Bench 2.0

    GLM-556.2%
    Source
    MiniMax M2.757.0%
    Source

    MiniMax M2.7 leads this result

  • Multi-SWE Bench

    GLM-5—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GLM-5—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-5—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    GLM-560.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AI-Needle

    GLM-563.3%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • GPQA

    GLM-586%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    GLM-586.0%
    Source
    MiniMax M2.787.0%
    Source

    MiniMax M2.7 leads this result

  • SuperGPQA

    GLM-566.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMLU-Pro

    GLM-585.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Shared source
    GLM-585.8%
    MiniMax M2.780.8%

    GLM-5 leads this result

  • HLE

    GLM-530.5%
    Source
    MiniMax M2.728%
    Source

    GLM-5 leads this result

  • GPQA Diamond (Vals)

    GLM-5—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    GLM-583.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • NOVA-63

    GLM-555.1%
    Source
    MiniMax M2.7—

    Not directly comparable

Instruction following

  • IFEval

    GLM-592.6%
    Source
    MiniMax M2.7—

    Not directly comparable

Math

  • AIME26

    GLM-595.8%
    Source
    MiniMax M2.789.8%
    Source

    GLM-5 leads this result

  • AIME25 (Arcee)

    Shared source
    GLM-593.3%
    MiniMax M2.780.0%

    GLM-5 leads this result

  • HMMT Feb 2025

    GLM-597.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HMMT Nov 2025

    GLM-596.9%
    Source
    MiniMax M2.781.0%
    Source

    GLM-5 leads this result

  • HMMT Feb 2026

    GLM-586.4%
    Source
    MiniMax M2.772.7%
    Source

    GLM-5 leads this result

  • MMAnswerBench

    GLM-582.5%
    Source
    MiniMax M2.7—

    Not directly comparable

48 public results · 19 shared

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Last updated October 29, 2026