Output tokens are the expensive ones
On almost every endpoint in this index, a generated token costs several times more than a supplied one. Across 864 priced endpoints, the median output price is 3.89× the input price — so the fastest way to cut a bill is usually to make the model say less, not to feed it less. Every number here is computed from published prices.
How lopsided is it?
Share of priced endpoints whose output price is at least N× their input price.
Is the premium growing? Every model by release date
One dot per model: release date against its median output/input price ratio across all hosts. Reasoning-heavy models tend to carry the steepest premiums — watch the upper band fill in over time. Click a brand to isolate it.
Trim output, not input
A chatbot workload (1,000 in / 500 out, 1,000 requests) on GLM 5.2 (CoreWeave)(CoreWeave) — a typical 3.9× output premium. Baseline: $1.83. Now cut 30% off one side:
Same 30% cut, but trimming the response saves 1.9× more. Tighten max_tokens, ask for terse answers, or stream-and-stop — it moves the bill more than shrinking the prompt.
Try it — trim output30% · trim input0% → cost $1.47()
Highest output premiums
Endpoints where output is priced furthest above input — where response length matters most.
| Model | Provider | Input /1M | Output /1M | Premium |
|---|---|---|---|---|
| Qwen3.8 27B (DekaLLM) | DekaLLM | $0.040 | $2.500 | 62.5× |
| Qwen3.8 27B (Modal) | Modal | $0.040 | $2.300 | 57.5× |
| Qwen3.8 27B (Reka) | Reka | $0.040 | $2.200 | 55× |
| GLM 5.2 (Cloudflare) | Cloudflare | $0.190 | $8.000 | 42.1× |
| Qwen3.8 27B (Chutes) | Chutes | $0.039 | $1.250 | 32.1× |
| GLM 5.3 (Reka) | Reka | $0.140 | $4.200 | 30× |
| GLM 5.3 Flash (Reka) | Reka | $0.080 | $2.400 | 30× |
| GLM 5.3 (AkashML) | AkashML | $0.190 | $4.400 | 23.2× |
| GLM 5.3 (Morph) | Morph | $0.266 | $4.400 | 16.5× |
| GLM 5.2 (Morph) | Morph | $0.266 | $4.400 | 16.5× |
| Qwen3.8 27B (Phala) | Phala | $0.200 | $2.500 | 12.5× |
| Qwen3.8 27B (DeepInfra) | DeepInfra | $0.150 | $1.875 | 12.5× |
| Qwen3 Next 80B A3B Instruct (DeepInfra) | DeepInfra | $0.090 | $1.100 | 12.2× |
| Qwen3 VL 30B A3B Thinking (Alibaba) | Alibaba | $0.200 | $2.400 | 12× |
| Qwen3 30B A3B Thinking 2507 (Alibaba) | Alibaba | $0.200 | $2.400 | 12× |
| Kimi K3 (Morph) | Morph | $1.252 | $14.900 | 11.9× |
| GLM 5.3 (DekaLLM) | DekaLLM | $0.250 | $2.950 | 11.8× |
| Qwen3 VL 8B Thinking (Alibaba) | Alibaba | $0.180 | $2.100 | 11.7× |
| Kimi K3 (AkashML) | AkashML | $1.200 | $14.000 | 11.7× |
| Qwen3 Next 80B A3B Instruct (Parasail) | Parasail | $0.100 | $1.100 | 11× |
Prices per 1M tokens (USD), from each provider's published input/output rates. Verified 2026-10-23.
FAQ
Do input and output tokens cost the same?
No. Across 864 priced endpoints, the median output-token price is 3.89× the input-token price. Generated tokens are the expensive side of the bill.
What's the cheapest way to cut an LLM API bill?
Usually to make the model say less, not to feed it less. Because output is priced about 3.89× input, trimming the response — tighter max_tokens, terse answers, stream-and-stop — moves the bill more than shrinking the prompt.
Why is output priced higher than input?
Output tokens are generated one at a time, each needing a full forward pass, while input tokens are processed in parallel during prefill. That compute asymmetry is passed through into per-token pricing.