per 1M tokens
Baichuan M2 32B
novita/baichuan/baichuan-m2-32b
Baichuan-M2 is a medically-enhanced reasoning model specifically designed for real-world medical reasoning tasks. We begin with real-world medical questions and conduct reinforcement learning training based on a large-scale verifier system. While maintaining the model's general capabilities, the medical effectiveness of Baichuan-M2 has achieved breakthrough improvements. Baichuan-M2 is currently the world's best open-source medical model. On the HealthBench Benchmark, it surpasses all open-source models, including GPT-OSS-120B, as well as many cutting-edge closed-source models. It is the open-source model closest to GPT-5 in terms of medical capabilities. Our research demonstrates that a robust verifier is crucial for aligning model capabilities with real-world applications, and an end-to-end reinforcement learning approach fundamentally enhances the model's medical reasoning abilities. The release of Baichuan-M2 represents a significant advancement in the field of medical artificial intelligence, pushing t
Price
per 1M tokens
Providers
Each row is one seller. Prices are the published rate from that source. A dash means that source did not publish the amount. Sorted by a 3:1 blend of input and output.
| Provider | Tag | Quant | Input | Output | Context | Uptime | Status | Source |
|---|---|---|---|---|---|---|---|---|
| Novita | — | — | $0.07/1M | $0.07/1M | 131K | — | Listed | novita |
Monthly bill
Token models use the token counts. Image, video, and audio models use units times the flat rate.
$0.14 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| token | $0.07/1M | $0.07/1M | Not published— | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-10-08 | token | $0.07 | $0.07 | Not published— |
| 2026-10-09 | token | $0.07 | $0.07 | Not published— |
| 2026-10-10 | token | $0.07 | $0.07 | Not published— |
| 2026-10-11 | token | $0.07 | $0.07 | Not published— |
| 2026-10-12 | token | $0.07 | $0.07 | Not published— |
| 2026-10-13 | token | $0.07 | $0.07 | Not published— |
| 2026-10-14 | token | $0.07 | $0.07 | Not published— |
| 2026-10-15 | token | $0.07 | $0.07 | Not published— |
| 2026-10-16 | token | $0.07 | $0.07 | Not published— |
| 2026-10-17 | token | $0.07 | $0.07 | Not published— |
| 2026-10-18 | token | $0.07 | $0.07 | Not published— |
| 2026-10-19 | token | $0.07 | $0.07 | Not published— |
| 2026-10-20 | token | $0.07 | $0.07 | Not published— |
| 2026-10-21 | token | $0.07 | $0.07 | Not published— |
| 2026-10-22 | token | $0.07 | $0.07 | Not published— |
| 2026-10-23 | token | $0.07 | $0.07 | Not published— |
| 2026-10-24 | token | $0.07 | $0.07 | Not published— |
| 2026-10-25 | token | $0.07 | $0.07 | Not published— |
| 2026-10-26 | token | $0.07 | $0.07 | Not published— |
| 2026-10-27 | token | $0.07 | $0.07 | Not published— |
| 2026-10-28 | token | $0.07 | $0.07 | Not published— |
| 2026-10-29 | token | $0.07 | $0.07 | Not published— |
| 2026-10-30 | token | $0.07 | $0.07 | Not published— |
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Call this model
OpenRouter id novita/baichuan/baichuan-m2-32b. InferenceList does not send the request.
curl
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"novita/baichuan/baichuan-m2-32b","messages":[{"role":"user","content":"Hello"}]}'
JavaScript
await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer " + process.env.OPENROUTER_API_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "novita/baichuan/baichuan-m2-32b",
messages: [{ role: "user", content: "Hello" }],
}),
});
Python
import os, json, urllib.request
req = urllib.request.Request(
"https://openrouter.ai/api/v1/chat/completions",
data=json.dumps({"model": "novita/baichuan/baichuan-m2-32b", "messages": [{"role": "user", "content": "Hello"}]}).encode(),
headers={"Authorization": "Bearer " + os.environ["OPENROUTER_API_KEY"], "Content-Type": "application/json"},
)
print(urllib.request.urlopen(req).read().decode())