What an LLM call actually costs in November 2026: open models per 1M tokens

# prices.py: live per-1M-token prices for the self-hosted models, plus a workload estimate import json, urllib.request with urllib.request.urlopen("https://synap402.com/v1/models") as r: models = json.load(r)["data"] # Self-hosted models are the ones with a batch price; aliases and routers have none. own = [m for m in models if m.get("pricing", {}).get("basis") == "engine" and "batch_prompt_per_million" in m["pricing"]] REQS, IN_TOK, OUT_TOK = 10_000, 2_000, 500 # the workload to price print(f"{'model':36} {'in/1M':>8} {'out/1M':>8} {'ctx':>8} {'realtime':>10} {'batch':>9}") for m in sorted(own, key=lambda m: m["pricing"]["prompt_per_million"]): p = m["pricing"] rt = REQS * (IN_TOK * p["prompt_per_million"] + OUT_TOK * p["completion_per_million"]) / 1e6 bt = REQS * (IN_TOK * p["batch_prompt_per_million"] + OUT_TOK * p["batch_completion_per_million"]) / 1e6 print(f"{m['id']:36} {p['prompt_per_million']:>8} {p['completion_per_million']:>8} " f"{m['context_length']:>8} {rt:>10.2f} {bt:>9.2f}") 

$ python3 prices.py model in/1M out/1M ctx realtime batch gpt-oss-20b 0.017 0.085 65536 0.77 0.38 gemma-4-26b-a4b-it 0.04 0.21 32768 1.85 0.93 qwen3-coder-30b 0.0675 0.27 98304 2.70 1.35 qwen/qwen3-30b-a3b-instruct-2507 0.081267 0.2438 98304 2.84 1.42 qwen/qwen3-vl-30b-a3b-instruct 0.14 0.55 65536 5.55 2.77 meta/muse-glimmer-30b 0.28 1.1 131072 11.10 5.55 

# measure.py: send the same real request to each model and price what came back import json, os, urllib.request from openai import OpenAI client = OpenAI(base_url="https://synap402.com/v1", api_key=os.environ["SYNAP_API_KEY"]) with urllib.request.urlopen("https://synap402.com/v1/models") as r: catalog = {m["id"]: m for m in json.load(r)["data"]} TICKET = ("Hi, I was charged twice for my October invoice (#4471) and the second " "charge still hasn't been refunded. Can someone look at this today?") PROMPT = ("Classify this support ticket. Answer with JSON only: " '{"category": "billing|bug|how-to|other", "urgent": true|false}\n\n' + TICKET) for mid in ["gpt-oss-20b", "gemma-4-26b-a4b-it", "qwen3-coder-30b", "qwen/qwen3-30b-a3b-instruct-2507", "meta/muse-glimmer-30b"]: r = client.chat.completions.create(model=mid, max_tokens=400, messages=[{"role": "user", "content": PROMPT}]) p, u = catalog[mid]["pricing"], r.usage cost = (u.prompt_tokens * p["prompt_per_million"] + u.completion_tokens * p["completion_per_million"]) / 1e6 answer = (r.choices[0].message.content or "").strip().replace("\n", " ") print(f"{mid:34} {u.prompt_tokens:>4} in {u.completion_tokens:>4} out ${cost:.7f} {answer[:60]}") 

gpt-oss-20b 128 in 178 out $0.0000173 {"category":"billing","urgent":true} gemma-4-26b-a4b-it 80 in 17 out $0.0000068 ``` json {"category": "billing", "urgent": true} 

 All five got it right. Scaled to 10,000 tickets: | model | per request | × 10,000 | |---|---|---| | gemma-4-26b-a4b-it | $0.0000068 | $0.07 | | qwen3-coder-30b | $0.0000081 | $0.08 | | qwen3-30b-a3b-instruct-2507 | $0.0000088 | $0.09 | | gpt-oss-20b | $0.0000173 | $0.17 | | muse-glimmer-30b | $0.0002517 | $2.52 | **The cheapest model per token came fourth per request.** gpt-oss-20b is a reasoning model. It produced 178 output tokens, most of them thinking, to write a 36-character answer. Its chat template also turned the same prompt into 128 input tokens where Qwen counted 72. The two Qwen models answered in 12 tokens. For short, structured answers, a non-reasoning model at 3–4× the token price came out at under half the cost. The opposite holds when the answer is long and the reasoning is what you pay for. Then gpt-oss-20b's $0.085 output price is hard to beat. Measure your own prompt. Don't rank models by the price column. Also note Gemma wrapped its JSON in a Markdown fence. If you parse the output, use `response_format` (Gemma, gpt-oss and qwen3-coder accept `json_schema` here) and don't strip fences by hand. ## 3. Batch: the same job at half price If nobody is waiting on the answer (nightly classification, evals, back-filling embeddings or summaries), send the requests as a batch. It's the standard OpenAI Batch API, so the official SDK works unchanged: ```python # batch.py: the same job at half price through the OpenAI Batch API import json, os, time from openai import OpenAI client = OpenAI(base_url="https://synap402.com/v1", api_key=os.environ["SYNAP_API_KEY"]) tickets = [ "I was charged twice for invoice #4471, please refund the second charge.", "How do I rotate my API key without downtime?", "The export button returns a 500 error since this morning.", ] with open("requests.jsonl", "w") as f: for i, t in enumerate(tickets): f.write(json.dumps({ "custom_id": f"t{i}", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen3-coder-30b", "max_tokens": 50, "messages": [{"role": "user", "content": 'Classify as JSON {"category": "billing|bug|how-to|other"}: ' + t}]}, }) + "\n") f = client.files.create(file=open("requests.jsonl", "rb"), purpose="batch") b = client.batches.create(input_file_id=f.id, endpoint="/v1/chat/completions", completion_window="24h") while b.status not in ("completed", "failed", "expired", "cancelled"): time.sleep(10) b = client.batches.retrieve(b.id) print(b.status, b.request_counts) for line in client.files.content(b.output_file_id).text.splitlines(): r = json.loads(line) body = r["response"]["body"] print(r["custom_id"], body["usage"]["prompt_tokens"], body["usage"]["completion_tokens"], body["choices"][0]["message"]["content"].strip()) 

completed BatchRequestCounts(completed=3, failed=0, total=3) t0 44 14 {"category": "billing"} t1 36 15 {"category": "how-to"} t2 40 14 {"category": "bug"}