Qwen3.8 Max vs GPT-6 Sol cost.
OpenAI's September price cut brought GPT-6 Sol level with Qwen3.8 Max on input price. The difference now sits in output and in the tokenizer.
- Input gap
- same
- Output gap
- 1.7×
- Cached gap
- 1.3×
- Rates verified
- 2026-10-02
Current-model note: Both models are current. GPT-6 Sol replaced GPT-5.6 Sol at half the price on 22 September 2026, and this page replaces the GPT-5.6 Sol comparison.
| Current model | Input / 1M | Cached / 1M | Cache write / 1M | Output / 1M | Context |
|---|---|---|---|---|---|
| Qwen3.8 MaxOfficial pricing ↗ | $2.00 | $0.25 | $2.50 | $6.00 | 1,000,000 |
| GPT-6 SolOfficial pricing ↗ | $2.00 | $0.20 | $2.50 | $10.00 | 1,050,000 |
Rates verified 2026-10-02. The browser may refresh them from the live catalogue. Provider pricing and tiers remain authoritative.
Qwen3.8 Max: Provider-Calibrated UTF-8 Projection
GPT-6 Sol: exact local BPE tokenization
PDF, DOCX, code, text, and data files are extracted locally and applied to both models.
Qwen3.8 Max: unavailable
GPT-6 Sol: documented formula
Prompt text, document contents, and image pixels stay in the browser.
Text, code, PDF, DOCX, images, audio, or video · 12 MB per file
Your input, measured across providers.
Choose a provider card to inspect every available model.
Workload & Scaling Planner +
What five real workloads actually cost
Headline per-token prices rarely decide a bill. Request shape does. These figures are calculated from the verified rates for Qwen3.8 Max and GPT-6 Sol, including any long-context tier that applies once a request crosses its threshold.
| Workload | Input | Output | Qwen3.8 Max | GPT-6 Sol | Difference |
|---|---|---|---|---|---|
| Short chat turn A typical assistant exchange. | 1,000 | 500 | $0.0050 | $0.0070 | 1.40x cheaper on Qwen3.8 Max |
| RAG answer Five retrieved chunks plus a question. | 12,000 | 800 | $0.029 | $0.032 | 1.11x cheaper on Qwen3.8 Max |
| Code review A medium pull request with surrounding files. | 60,000 | 2,000 | $0.132 | $0.140 | 1.06x cheaper on Qwen3.8 Max |
| Whole-document analysis A long report or contract read in one call. | 300,000 | 4,000 | $0.624 | $1.26 (tier) | 2.02x cheaper on Qwen3.8 Max |
| Full-context load Filling most of a one-million-token window. | 900,000 | 4,000 | $1.82 | $3.66 (tier) | 2.01x cheaper on Qwen3.8 Max |
"(tier)" marks a request that crossed a long-context threshold, so it is billed above the headline rate. Output length is the assumption most worth changing for your own case: paste a real prompt into the calculator above to replace these with your numbers.
The cached-input rate is the number most comparisons miss
Agents, chat threads and RAG pipelines resend the same system prompt and context on every call. Those repeated tokens bill at the cached rate, not the headline rate, so a model can be cheaper on paper and more expensive in production.
| Model | Fresh input / 1M | Cached input / 1M | Discount | Break-even |
|---|---|---|---|---|
| Qwen3.8 Max | $2.00 | $0.250 | 88% | 1 read |
| GPT-6 Sol | $2.00 | $0.200 | 90% | 1 read |
Break-even assumes a cache write costs about 1.25x the base input rate, which is what OpenAI and Anthropic currently document for a short time-to-live. It answers one question: how many times a cached prefix must be re-read before caching is cheaper than paying full price each call. Above that count, every further read saves the discount shown.
Identical input price, 40 percent less on output
Qwen3.8 Max lists $2 per million input tokens and $6 per million output. GPT-6 Sol lists $2 and $10. Until September the OpenAI side of this comparison cost $4 and $20, so the input gap has closed completely and the output gap has narrowed from more than three times to 1.7 times.
Qwen3.8 Max carries a one-million-token context window at a flat rate, while GPT-6 Sol applies a long-context tier above 272,000 input tokens that doubles input and raises output by half. On large single requests Qwen is still clearly cheaper.
Tokenizer vocabulary changes the count, not just the rate
Qwen's vocabulary is trained for Chinese and English together. English prose therefore packs a slightly different number of UTF-8 bytes per token than it does on an OpenAI encoding, and Chinese text differs much more.
The consequence is practical: a cost ratio you measured on English documents will not transfer to a Chinese corpus. If your workload is multilingual, run representative text through the calculator for each language rather than applying one multiplier.
This site measures Qwen with a deterministic byte-length projection calibrated for its family and labels it as an estimate. OpenAI text is counted exactly in your browser.
Benchmarks have narrowed; task evaluation has not
Published 2026 comparisons place Qwen3.8-Max among the open-weight models scoring above 91 percent on GPQA Diamond, alongside Kimi K3 and GLM-5.2. On several reasoning benchmarks the gap to frontier proprietary models is now small.
That is a reason to run the comparison, not a reason to skip it. Benchmark parity and task parity are different things, and the only evidence that settles your case is a held-out evaluation on your own data.
Model the workload, not the marketing price.
Input prices now match, so compare output volume and token counts. If your content is substantially non-English, measure token counts on real text rather than trusting a ratio measured on English.
Review calculation methodology →Measure against the model you use
Other comparisons worth running
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Frequently asked questions
Is Qwen3.8 Max cheaper than GPT-6 Sol?+
They match at $2 per million input tokens. Qwen3.8 Max is cheaper on output, $6 against $10 per million, and it has no long-context surcharge, while GPT-6 Sol doubles input above 272,000 tokens.
What happened to the GPT-5.6 Sol comparison?+
GPT-6 Sol replaced it on 22 September 2026 at half the price. This page replaces the earlier comparison and the old address redirects here.
Do Qwen token counts differ from OpenAI counts for the same text?+
Yes. Qwen's vocabulary is trained for Chinese and English together, so the bytes-per-token ratio differs from an OpenAI encoding, and the difference is much larger on Chinese text than on English. Measure each language separately.