DeepSeek V4 Flash vs GLM-5
Head-to-head specifications
| Metric | DeepSeek V4 Flash | GLM-5 | Difference |
|---|---|---|---|
| Intelligence Index | 40.0 | 33.0 | +21.2% |
| Context window | 1M tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.06 | $0.52 | -88.5% |
| Output speed (tokens/s) | 102 | 46 | +121.7% |
| Access | Open weights | Open weights | — |
- DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 33.0).
- DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 8.7× cheaper.
- DeepSeek V4 Flash offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: DeepSeek V4 Flash or GLM-5?
DeepSeek V4 Flash advantages
- General intelligence (+18%)
- Context window (+74%)
- Affordability (+88%)
- Output speed (+55%)
GLM-5 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the DeepSeek V4 Flash if you need the strongest overall reasoning and accuracy.
- Choose the DeepSeek V4 Flash if you work with long documents or large codebases.
Value for money
DeepSeek V4 Flash offers more intelligence per dollar (10.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
DeepSeek V4 Flash vs GLM-5: which should you choose?
DeepSeek V4 Flash — DeepSeek text model with an Intelligence Index of 40, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
GLM-5 — Z.ai (Zhipu) text model with an Intelligence Index of 33, a 256K-token context window and a blended price of $0.52/1M tokens (open weights).
DeepSeek V4 Flash vs GLM-5: DeepSeek V4 Flash scores higher on the Intelligence Index. DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 33.0). DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 8.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the DeepSeek V4 Flash scores 40.0 versus 33.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The DeepSeek V4 Flash accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, DeepSeek V4 Flash generates faster (102 vs 46 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, DeepSeek V4 Flash is the cheaper model to run ($0.06 vs $0.52 per 1M tokens). DeepSeek V4 Flash is open weights and GLM-5 is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the DeepSeek V4 Flash better than the GLM-5?
DeepSeek V4 Flash is the clearly stronger overall choice, winning most of the dimensions that matter. DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 33.0).
What is the main difference between the DeepSeek V4 Flash and the GLM-5?
DeepSeek V4 Flash leads overall capability (Intelligence Index 40.0 vs 33.0). DeepSeek V4 Flash is the cheaper model to run at $0.06/1M blended tokens — about 8.7× cheaper.
Which is better value?
DeepSeek V4 Flash offers more intelligence per dollar (10.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the DeepSeek V4 Flash if you need the strongest overall reasoning and accuracy. Choose the DeepSeek V4 Flash if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.