Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 80B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 128k | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.072DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.88DeepInfra |
| — | $73.00DeepInfra · fp8 | |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Feb 12 2026 | Sep 11 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
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GPT-5.3-Codex-Spark and Qwen3-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3-Next shipped 154 days before GPT-5.3-Codex-Spark, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (GPT-5.3-Codex-Spark) vs 256k (Qwen3-Next). GPT-5.3-Codex-Spark is closed, while Qwen3-Next is open weight.
Direct benchmark comparisons are unavailable — GPT-5.3-Codex-Spark and Qwen3-Next don't publish scores on any of the same benchmarks.