GPT-4 TurbovsQwen2.5-Coder
GPT-4 Turbo | Qwen2.5-Coder | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 32B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 128k | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $10.00 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $30.00 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.66Cloudflare |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.00Cloudflare |
| BenchmarksPublished by one model only | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 42.5% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Nov 6 2023 | Nov 12 2024 |
| Access | Proprietary | Open Weight |
Other comparisons
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Frequently asked questions
GPT-4 Turbo and Qwen2.5-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4 Turbo has a verified first-party API price: $10.00 per million input tokens and $30.00 per million output tokens. No pay-as-you-go API rate is tracked for Qwen2.5-Coder. GPT-4 Turbo shipped 372 days before Qwen2.5-Coder, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (GPT-4 Turbo) vs 128k (Qwen2.5-Coder). GPT-4 Turbo is proprietary, while Qwen2.5-Coder is open weight.
Direct benchmark comparisons are unavailable — GPT-4 Turbo and Qwen2.5-Coder don't publish scores on any of the same benchmarks.