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. | — | 32B |
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 | 128k |
| These models have no shared benchmark scores. | ||
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 63.4% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Feb 12 2026 | Mar 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-5.3-Codex-SparkvsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5GPT-5.3-Codex-SparkvsGemini 4 CarbonQwQ-32BvsGemini 4 CarbonGPT-5.3-Codex-SparkvsMuse Spark OpenQwQ-32BvsMuse Spark OpenGPT-5.3-Codex-SparkvsGrok 4.7QwQ-32BvsGrok 4.7GPT-5.3-Codex-SparkvsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017GPT-5.3-Codex-SparkvsMistral Large 4QwQ-32BvsMistral Large 4Frequently asked questions
GPT-5.3-Codex-Spark and QwQ-32B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. QwQ-32B shipped 343 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 128k (QwQ-32B). GPT-5.3-Codex-Spark is closed, while QwQ-32B is open weight.
Direct benchmark comparisons are unavailable — GPT-5.3-Codex-Spark and QwQ-32B don't publish scores on any of the same benchmarks.