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BenchLM

GLM-5.3 vs Step 3.7 Flash

Updated September 28, 2026. Rank says GLM-5.3 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-5.3 has the higher public score, 65.44 versus 41.33, and the 90% score intervals do not overlap. 3 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-5.3

Z.AI

65.44/100

Estimated · Public rank #27

90% interval 59.7–71.2

Model B
StepFun logo
Step 3.7 Flash

StepFun

41.33/100

Estimated · Public rank #111

90% interval 29.8–52.9

Shared results
3
GLM-5.3 only
21
Step 3.7 Flash only
8
Like-for-like categories
0 / 8
Estimated: GLM-5.3 and Step 3.7 FlashHow 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

    GLM-5.3

    GLM-5.3 has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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.

56.7GLM-5.332.0Step 3.7 Flash

Directional only · BenchAlign v5.7

GLM-5.3 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

3 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.

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 v5.7 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-5.3
67.3
Supported · #9/111
Step 3.7 Flash
35.3
Estimated · #60/111
Basis
BenchAlign v5.7 lane · 9 vs 7 public rows
Reading
Directional only

Coding

Directional only
GLM-5.3
56.7
Supported · #22/136
Step 3.7 Flash
32.0
Estimated · #82/136
Basis
BenchAlign v5.7 lane · 13 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
GLM-5.3
61.9
Supported · #33/160
Step 3.7 Flash
43.2
Estimated · #79/160
Basis
BenchAlign v5.7 lane · 2 vs 0 public rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.3
77.1
#11/27
Step 3.7 Flash
73.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.3
Not ranked
Step 3.7 Flash
72.0
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.3
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.3
Not ranked
Step 3.7 Flash
80.6
#53/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5.3
Not ranked
Step 3.7 Flash
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 v5.7) 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-5.3
Self-hosted; infrastructure cost varies
Fits in one request
Step 3.7 Flash
$0.00077
Fits in one request

GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request
Step 3.7 Flash
$0.01345
Fits in one request

GLM-5.3 has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Step 3.7 Flash
$0.0555
Fits in one request
Cached input priced at the published list-input rate

Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3 has no comparable published API token 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-5.3

1M

Z.AI GLM-5.3 model card

Step 3.7 Flash

256K

Cached-input rate

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

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Step 3.7 Flash

Not published

Documented inputs

GLM-5.3

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

GLM-5.3

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

GLM-5.3

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Step 3.7 Flash

Reasoning

Weight access

GLM-5.3

Open Weight

Step 3.7 Flash

Open Weight

License

GLM-5.3

Open Weight

Step 3.7 Flash

Open Weight

Release date

GLM-5.3

2026-08-14

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-5.3 has the higher public score, 65.44 versus 41.33, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GLM-5.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-5.3 or Step 3.7 Flash?

GLM-5.3 has the higher public score, 65.44 versus 41.33, 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-5.3 or Step 3.7 Flash?

GLM-5.3 scores higher for coding on the public lane, 56.7 to 32. 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-5.3 or Step 3.7 Flash?

GLM-5.3 scores higher for agentic tasks on the public lane, 67.3 to 35.3. 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-5.3 or Step 3.7 Flash?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GLM-5.3 or Step 3.7 Flash?

GLM-5.3 has the larger documented context window: 1M, compared with 256K.

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 2.1

    GLM-5.388.2%
    Source
    Step 3.7 Flash59.5%
    Source

    GLM-5.3 leads this result

  • terminalBench3

    GLM-5.328.3%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • CyberGym

    GLM-5.384.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • ExploitGym

    GLM-5.315.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Toolathlon-Verified

    GLM-5.373.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • AutomationBench

    GLM-5.348.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Agents' Last Exam

    GLM-5.328.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • HLE w/ tools

    GLM-5.362.5%
    Source
    Step 3.7 Flash47.2%
    Source

    GLM-5.3 leads this result

  • Terminal-Bench 2.1 (Vals)

    GLM-5.371.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • BrowseComp

    GLM-5.3—
    Step 3.7 Flash75.8%
    Source

    Not directly comparable

  • DeepSearchQA

    GLM-5.3—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Toolathlon

    GLM-5.3—
    Step 3.7 Flash49.5%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-5.3—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5.3—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    GLM-5.388.2%
    Source
    Step 3.7 Flash59.5%
    Source

    GLM-5.3 leads this result

  • terminalBench3

    GLM-5.328.3%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • DeepSWE

    GLM-5.366.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • NL2Repo

    GLM-5.358%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • ProgramBench

    GLM-5.319.0%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • FrontierSWE

    GLM-5.378.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • sweMarathon

    GLM-5.342.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • PostTrain Bench

    GLM-5.339.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • VulcanBench v3

    GLM-5.378.3%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.360.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • FrontierSWE v2

    GLM-5.330.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.380.5%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5.395.4%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench Pro

    GLM-5.3—
    Step 3.7 Flash56.3%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    GLM-5.3—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    GLM-5.3—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    GLM-5.388.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5.386.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

32 public results · 3 shared

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Last updated September 28, 2026