GLM-4.7 vs Step-3.5-Flash
GLM-4.7 and Step-3.5-Flash are closely matched at 34.3 and 37.2 on the LLM Stats Score. Step-3.5-Flash is 4.2x cheaper per token.
Zhipu AI · StepFun · Updated for 2026
Which is better?
GLM-4.7 and Step-3.5-Flash are closely matched on the overall LLM Stats Score at 34.3 and 37.2.
In the 7 individual benchmarks reported for both models, Step-3.5-Flash wins 7; this is a narrower head-to-head signal than the composite indexes.
On price, Step-3.5-Flash is roughly 4.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.7 also accepts a larger context window (202,752 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-4.7
- you process long inputs — it offers a 202,752 token context window
Choose Step-3.5-Flash
- you value its reported benchmark strengths — it wins 7 of 7 exact shared results
- cost matters — it's about 4.2x cheaper per token
- you want the most recent training data — it shipped Feb 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
13 reported for GLM-4.7 · 7 for Step-3.5-Flash
GLM-4.7 outperforms in 0 benchmarks, while Step-3.5-Flash is better at 7 benchmarks (AIME 2025, BrowseComp, IMO-AnswerBench, LiveCodeBench v6, SWE-Bench Verified, Tau-bench, Terminal-Bench 2.0).
Step-3.5-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.40/1M tokens) is 4.0x more expensive than Step-3.5-Flash ($0.10/1M tokens).
For output processing, GLM-4.7 ($1.75/1M tokens) is 4.4x more expensive than Step-3.5-Flash ($0.40/1M tokens).
In conclusion, GLM-4.7 is more expensive than Step-3.5-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.7 has 162.0B more parameters than Step-3.5-Flash, making it 82.7% larger.
Context Window
Maximum input and output token capacity
GLM-4.7 accepts 202,752 input tokens compared to Step-3.5-Flash's 65,536 tokens. GLM-4.7 can generate longer responses up to 202,752 tokens, while Step-3.5-Flash is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
GLM-4.7 supports multimodal inputs, whereas Step-3.5-Flash does not.
GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7
Step-3.5-Flash
License
Usage and distribution terms
GLM-4.7 is licensed under MIT, while Step-3.5-Flash uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.7 was released on 2025-12-22, while Step-3.5-Flash was released on 2026-02-02.
Step-3.5-Flash is 1 month newer than GLM-4.7.
Dec 22, 2025
9 months ago
Feb 2, 2026
7 months ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.7 is available from DeepInfra, Fireworks, Novita. Step-3.5-Flash is available from StepFun.
GLM-4.7
Step-3.5-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.7 and Step-3.5-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.7 vs Step-3.5-Flash.