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BenchLM
Z.AI logo
Model A
GLM-5.2

Z.AI

68.19/100

Supported · Public rank #28

90% interval 61.7–74.7

GLM-5.2 vs Step 3.7 Flash

Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

StepFun logo
Model B
Step 3.7 Flash

StepFun

50.02/100

Estimated · Public rank #126

90% interval 36.2–61.5

Decision reading

GLM-5.2 has the higher public score, 68.19 versus 50.02, and the 90% score intervals do not overlap.

4 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

    GLM-5.2

    GLM-5.2 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Step 3.7 Flash

    Step 3.7 Flash 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

    Step 3.7 Flash

    Step 3.7 Flash has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Step 3.7 Flash

    Step 3.7 Flash 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

    Step 3.7 Flash 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

    Step 3.7 Flash 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
4
GLM-5.2 only
21
Step 3.7 Flash only
7
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
GLM-5.2
58.5
Supported · #29/153
Step 3.7 Flash
48.6
Estimated · #65/153
Basis
BenchAlign lane · 6 vs 7 public rows
Reading
Directional only

Coding

Directional only
GLM-5.2
61.0
Supported · #19/152
Step 3.7 Flash
47.2
Estimated · #74/152
Basis
BenchAlign lane · 8 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
GLM-5.2
60.7
Supported · #35/183
Step 3.7 Flash
48.5
Estimated · #93/183
Basis
BenchAlign lane · 6 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
GLM-5.2
89.8
#22/123
Step 3.7 Flash
82.1
#52/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
74.8
Unranked · 2 rankable rows
Step 3.7 Flash
71.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5.2
80.7
Unranked · 4 rankable rows
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not ranked
Step 3.7 Flash
70.9
Unranked · 3 rankable rows
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.

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.

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-5.2
$0.0036
Fits in one request
Step 3.7 Flash
$0.00077
Fits in one request

Step 3.7 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.2
$0.0832
Fits in one request
Step 3.7 Flash
$0.01345
Fits in one request

Step 3.7 Flash 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-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate
Step 3.7 Flash
$0.0555
Fits in one request
Cached input priced at the published list-input rate

Step 3.7 Flash has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Step 3.7 Flash 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-5.2

1M

Step 3.7 Flash

256K

API model ID

GLM-5.2

Not sourced

Step 3.7 Flash

Not sourced

Cached-input rate

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

GLM-5.2

Not published

Step 3.7 Flash

Not published

Documented inputs

GLM-5.2

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

GLM-5.2

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

GLM-5.2

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

GLM-5.2

Reasoning

Step 3.7 Flash

Reasoning

Weight access

GLM-5.2

Open Weight

Step 3.7 Flash

Open Weight

License

GLM-5.2

Open Weight

Step 3.7 Flash

Open Weight

Release date

GLM-5.2

2026-06-16

Step 3.7 Flash

2026-05-29

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-5.2 has the higher public score, 68.19 versus 50.02, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.0832 vs $0.01345. Cache-heavy agent loop: $0.352 vs $0.0555.
Context tradeoff
GLM-5.2 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

  • Terminal-Bench 3.0

    GLM-5.24.6%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281%
    Source
    Step 3.7 Flash59.5%
    Source

    GLM-5.2 leads this result

  • MCP Atlas

    GLM-5.276.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Toolathlon

    GLM-5.248.2%
    Source
    Step 3.7 Flash49.5%
    Source

    Step 3.7 Flash leads this result

  • ResearchClawBench

    GLM-5.220.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5.267.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • BrowseComp

    GLM-5.2—
    Step 3.7 Flash75.8%
    Source

    Not directly comparable

  • DeepSearchQA

    GLM-5.2—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-5.2—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • HLE w/ tools

    GLM-5.2—
    Step 3.7 Flash47.2%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5.2—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    Step 3.7 Flash56.3%
    Source

    GLM-5.2 leads this result

  • NL2Repo

    GLM-5.248.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281.0%
    Source
    Step 3.7 Flash59.5%
    Source

    GLM-5.2 leads this result

  • ProgramBench

    GLM-5.263.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • cursorBench32

    GLM-5.255.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.258.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.269.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5.282.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • GPQA-D

    GLM-5.291.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HLE

    GLM-5.254.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-5.285.6%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5.286.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

Math

  • AIME26

    GLM-5.299.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

Multimodal

  • SimpleVQA

    GLM-5.2—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    GLM-5.2—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.2 or Step 3.7 Flash?

GLM-5.2 has the higher public score, 68.19 versus 50.02, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GLM-5.2 or Step 3.7 Flash?

GLM-5.2 scores higher for coding on the public lane, 61 to 47.2. Step 3.7 Flash 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-5.2 or Step 3.7 Flash?

GLM-5.2 scores higher for agentic tasks on the public lane, 58.5 to 48.6. Step 3.7 Flash 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-5.2 or Step 3.7 Flash?

For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00077 on Step 3.7 Flash; repository review costs $0.0832 and $0.01345; the cache-heavy agent loop costs $0.352 and $0.0555. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5.2 or Step 3.7 Flash?

GLM-5.2 has the larger documented context window: 1M, compared with 256K.

Related comparisons

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Last updated September 14, 2026

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