As of August 20, 2026, Qwen3 32B is 61% cheaper per million tokens than MAI-Code-1.1-Flash. MAI-Code-1.1-Flash is $0.20 / $1.20 per million input/output tokens versus Qwen3 32B at $0.10 / $0.44. Sources: https://ai.azure.com and https://bailian.console.aliyun.com/
Sources: Microsoft pricing (https://ai.azure.com); Qwen pricing (https://bailian.console.aliyun.com/)
MAI-Code-1.1-Flash
Microsoft
Input
$0.20
Output
$1.20
Qwen3 32B
Qwen
Input
$0.10
Output
$0.44
Token pricing, specs, and benchmarks side by side
MAI-Code-1.1-FlashQwen3 32BVerdict
Both models are closely matched on benchmarks. Qwen3 32B has the pricing advantage, making it the better value unless MAI-Code-1.1-Flash's specific capabilities are critical to your workflow.
As of August 20, 2026, Qwen3 32B is 61% cheaper on blended LLM API pricing. Qwen3 32B costs $0.10 per million input tokens and $0.44 per million output tokens ($0.54 blended 1M-in + 1M-out). MAI-Code-1.1-Flash costs $0.20 / $1.20 per million tokens ($1.40 blended). Sources: https://ai.azure.com and https://bailian.console.aliyun.com/. The cheapest LLM for your app still depends on how many output tokens you generate.
MAI-Code-1.1-Flash Microsoft API pricing is $0.20 input and $1.20 output per million tokens via Microsoft. Qwen3 32B Qwen API pricing is $0.10 input and $0.44 output per million tokens via Qwen. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
MAI-Code-1.1-Flash has a published SWE-bench Verified score of 72.6%. Qwen3 32B does not have that eval on this page yet.
MAI-Code-1.1-Flash supports a 256K token context window, while Qwen3 32B supports 128K tokens. MAI-Code-1.1-Flash offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Shared benchmark scores for MAI-Code-1.1-Flash and Qwen3 32B are still limited. Check the comparison table for the evals each model has published. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both MAI-Code-1.1-Flash and Qwen3 32B are production API models. For cost-sensitive production traffic, Qwen3 32B has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call MAI-Code-1.1-Flash through Microsoft and Qwen3 32B through Qwen with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published Microsoft API pricing and Qwen API pricing, quoted per million tokens. Official rate sheets: https://ai.azure.com and https://bailian.console.aliyun.com/. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
MAI-Code-1.1-Flash supports up to an unspecified number of output tokens per request, while Qwen3 32B supports up to 128K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Qwen3 32B has a measured throughput of approximately 32.43 tokens/second. Throughput data for MAI-Code-1.1-Flash is not yet available. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open MAI-Code-1.1-Flash or Qwen3 32B from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. MAI-Code-1.1-Flash costs $0.20/M input and $1.20/M output. Qwen3 32B costs $0.10/M input and $0.44/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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