DeepSeek-V4.1-Flash vs GLM-4.7
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 34.3. DeepSeek-V4.1-Flash is 2.2x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 34.3, ranking #13 overall.
The models split the 2 individual benchmarks reported for both models evenly.
On price, DeepSeek-V4.1-Flash is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 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-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 2.2x cheaper per token
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose GLM-4.7
- you want predictable pricing at $0.40/M input and $1.75/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 13 for GLM-4.7
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while GLM-4.7 is better at 1 benchmark (Humanity's Last Exam).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 1.8x cheaper than GLM-4.7 ($0.40/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 2.7x cheaper than GLM-4.7 ($1.75/1M tokens).
In conclusion, GLM-4.7 is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 405.2B more parameters than GLM-4.7, making it 113.2% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to GLM-4.7's 202,752 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while GLM-4.7 is limited to 202,752 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and GLM-4.7 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
GLM-4.7
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-V4.1-Flash was released on 2026-09-10, while GLM-4.7 was released on 2025-12-22.
DeepSeek-V4.1-Flash is 9 months newer than GLM-4.7.
Sep 10, 2026
1 weeks ago
8mo newerDec 22, 2025
9 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-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. GLM-4.7 is available from DeepInfra, Fireworks, Novita.
DeepSeek-V4.1-Flash
GLM-4.7
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and GLM-4.7 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs GLM-4.7.