glm-5.3-flash API — compare providers

The same model (glm-5.3-flash) is served by 19 providers; Relace is the cheapest at $0.309/1M blended — the priciest (Cloudflare) costs 110% more.

LicenseMITopen weights · permissive · terms
Knowledge cutoffJanuary 2026source · ranked
Intelligence57.5Artificial Analysis Intelligence Index · source · ranked

Quality and intelligence scores are attached to the model itself — identical whichever provider serves it.

RankProviderInput /1MOutput /1MBlended /1M
1Relace$0.0713$0.2375$0.309 ← cheapest
2GMICloud$0.075$0.25$0.325
3Novita AI$0.075$0.25$0.325
4DeepInfra$0.075$0.25$0.325
5Z.AI$0.075$0.25$0.325
6Morph$0.13$0.45$0.58
7Modal$0.15$0.4999$0.65
8Fireworks AI$0.15$0.5$0.65
9Phala$0.15$0.5$0.65
10Friendli$0.15$0.5$0.65
11SiliconFlow$0.15$0.5$0.65
12DigitalOcean$0.15$0.5$0.65
13Together AI$0.15$0.5$0.65
14Reka$0.15$0.5$0.65
15Parasail$0.15$0.5$0.65
16BaseTen$0.15$0.5$0.65
17Venice$0.15$0.5$0.65
18Io Net$0.15$0.5$0.65
19Cloudflare$0.15$0.5$0.65

Prices are per 1M tokens (USD). "Blended" = input + output, for coarse ranking. Same underlying model, 19 serving providers, 110% spread top to bottom. Context window: 1,310,720 tokens (131,072 max output) — see how it ranks in biggest context windows. See how this compares across the catalogue in same model, different price.

Cost per 1,000 requests by workload

What each provider actually bills for a representative job, not just the sticker price.

ProviderChatbot
1000 in / 500 out
RAG / long context
8000 in / 500 out
Batch summarize
4000 in / 1000 out
Relace$0.19$0.69$0.52
GMICloud$0.20$0.72$0.55
Novita AI$0.20$0.72$0.55
DeepInfra$0.20$0.72$0.55
Z.AI$0.20$0.72$0.55
Morph$0.35$1.27$0.97
Modal$0.40$1.45$1.10
Fireworks AI$0.40$1.45$1.10
Phala$0.40$1.45$1.10
Friendli$0.40$1.45$1.10
SiliconFlow$0.40$1.45$1.10
DigitalOcean$0.40$1.45$1.10
Together AI$0.40$1.45$1.10
Reka$0.40$1.45$1.10
Parasail$0.40$1.45$1.10
BaseTen$0.40$1.45$1.10
Venice$0.40$1.45$1.10
Io Net$0.40$1.45$1.10
Cloudflare$0.40$1.45$1.10

Estimate your own workload

Input tokens/request:   Output tokens/request:   Requests:

ProviderEstimated cost (USD)

Self-host / quantized builds

Open weights under MIT — you can run glm-5.3-flash on your own hardware instead of paying per token. Quantized builds lower the memory footprint; whether it fits your GPU or Mac depends on the model's size. Formats: GGUF for llama.cpp / Ollama / LM Studio, AWQ and GPTQ for GPU serving, MLX for Apple silicon.

Links search the live model hubs (Hugging Face, Ollama) so they track new builds as they appear — we don't host weights. Which formats exist depends on what the model owner and community have published.