DeepSeek-V4-Flash-0731 vs GLM-5
DeepSeek-V4-Flash-0731 and GLM-5 are closely matched at 44.7 and 37.1 on the LLM Stats Score. DeepSeek-V4-Flash-0731 is 17.2x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and GLM-5 are closely matched on the overall LLM Stats Score at 44.7 and 37.1.
On price, DeepSeek-V4-Flash-0731 is roughly 17.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 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 DeepSeek-V4-Flash-0731
- your work emphasizes agents — it leads those capability indexes
- cost matters — it's about 17.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
Choose GLM-5
- you want predictable pricing at $1.00/M input and $3.20/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 5 for GLM-5
DeepSeek-V4-Flash-0731 and GLM-5don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.06/1M tokens) is 16.7x cheaper than GLM-5 ($1.00/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 17.8x cheaper than GLM-5 ($3.20/1M tokens).
In conclusion, GLM-5 is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 440.0B more parameters than DeepSeek-V4-Flash-0731, making it 144.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to GLM-5's 200,000 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 1,048,576 tokens, while GLM-5 is limited to 128,000 tokens.
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while GLM-5 was released on 2026-02-11.
DeepSeek-V4-Flash-0731 is 6 months newer than GLM-5.
Jul 31, 2026
1 months ago
5mo newerFeb 11, 2026
7 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. GLM-5 is available from FriendliAI, ZAI.
DeepSeek-V4-Flash-0731
GLM-5
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and GLM-5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs GLM-5.