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BenchLM
Z.AI logo
Model A
GLM-5-Turbo

Z.AI

61.69/100

Supported · Public rank #54

90% interval 48.4–75.0

GLM-5-Turbo vs MiniMax M2.7

Updated September 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.14/100

Supported · Public rank #95

90% interval 43.8–66.5

Decision reading

GLM-5-Turbo has the higher public score estimate, 61.69 versus 55.14, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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

    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-Turbo 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

    GLM-5-Turbo is not ranked on the public lane for agentic, so no winner is named for agentic.

    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-Turbo does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5-Turbo 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-Turbo only
0
MiniMax M2.7 only
22
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.

Knowledge

Directional only
GLM-5-Turbo
53.9
Estimated · #61/183
MiniMax M2.7
48.7
Supported · #90/183
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Directional only

Instruction following

Directional only
GLM-5-Turbo
89.7
#24/123
MiniMax M2.7
93.0
#10/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
GLM-5-Turbo
Not ranked
MiniMax M2.7
41.1
Estimated · #110/153
Basis
BenchAlign lane · 1 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
GLM-5-Turbo
Not ranked
MiniMax M2.7
48.6
Estimated · #68/152
Basis
BenchAlign lane · 0 vs 11 public rows
Reading
Not comparable

Reasoning

Not comparable
GLM-5-Turbo
70.3
Unranked · 2 rankable rows
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

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

Multilingual

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

Multimodal

Not comparable
GLM-5-Turbo
Not ranked
MiniMax M2.7
Not ranked
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.

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-Turbo
$0.0032
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-Turbo
$0.072
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-Turbo
$0.304
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-Turbo does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5-Turbo 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-Turbo

200K

MiniMax M2.7

200K

API model ID

GLM-5-Turbo

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-Turbo

Not published

MiniMax M2.7

Not published

Documented inputs

GLM-5-Turbo

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GLM-5-Turbo

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GLM-5-Turbo

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GLM-5-Turbo

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GLM-5-Turbo

Proprietary

MiniMax M2.7

Open Weight

License

GLM-5-Turbo

Proprietary

MiniMax M2.7

Open Weight

Release date

GLM-5-Turbo

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
GLM-5-Turbo has the higher public score estimate, 61.69 versus 55.14, but the 90% score intervals overlap.
Workload cost
Repository review: $0.072 vs $0.0186. Cache-heavy agent loop: $0.304 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 evidence23 rows

Agentic

  • Claw-Eval

    Shared source
    GLM-5-Turbo55.8%
    MiniMax M2.748.7%

    GLM-5-Turbo leads this result

  • Terminal-Bench 2.0

    GLM-5-Turbo—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    GLM-5-Turbo—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GLM-5-Turbo—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GLM-5-Turbo—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5-Turbo—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5-Turbo—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified*

    GLM-5-Turbo—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GLM-5-Turbo—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    GLM-5-Turbo—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GLM-5-Turbo—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GLM-5-Turbo—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GLM-5-Turbo—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GLM-5-Turbo—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-5-Turbo—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    GLM-5-Turbo—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5-Turbo—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5-Turbo—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GLM-5-Turbo—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GLM-5-Turbo—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-5-Turbo—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5-Turbo—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    GLM-5-Turbo—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

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

GLM-5-Turbo has the higher public score estimate, 61.69 versus 55.14, 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-Turbo or MiniMax M2.7?

GLM-5-Turbo is not ranked on the public lane for coding, so no winner is named for coding.

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

GLM-5-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-5-Turbo or MiniMax M2.7?

For the stated presets, chat costs $0.0032 on GLM-5-Turbo and $0.0009 on MiniMax M2.7; repository review costs $0.072 and $0.0186; the cache-heavy agent loop costs $0.304 and $0.078. GLM-5-Turbo does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. GLM-5-Turbo 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-Turbo or MiniMax M2.7?

Both models list the same context window, 200K.

Related comparisons

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Last updated September 15, 2026

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