Qwen3 235B A22B Thinking 2507 is $0.11 cheaper per 1M input tokens (41.4% lower; 1.71x difference).
API cost decision in 10 seconds
MiniMax M2 vs Qwen3 235B A22B Thinking 2507
Pick MiniMax M2 for lower cost; pick Qwen3 235B A22B Thinking 2507 only if the larger context window matters more.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick MiniMax M2 for lower cost; pick Qwen3 235B A22B Thinking 2507 only if the larger context window matters more.
On the standard 1M input plus 500K output workload, MiniMax M2 is estimated at $0.76 vs $0.9 for Qwen3 235B A22B Thinking 2507, saving $0.14 (15.8% lower).
Cost-first pickMiniMax M2
Context-first pickQwen3 235B A22B Thinking 2507
Sample savings$0.1415.8%
10x traffic gap$1.42
Qwen3 235B A22B Thinking 2507 has more context, but MiniMax M2 saves $0.14 on the standard workload. At 10x that traffic, the same price gap is about $1.42. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Cost winner changes by workload shape: input-heavy / RAG favors Qwen3 235B A22B Thinking 2507, balanced workload favors MiniMax M2, and output-heavy chatbot favors MiniMax M2.
| Workload shape | Token mix | Better pick | MiniMax M2 | Qwen3 235B A22B Thinking 2507 |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Qwen3 235B A22B Thinking 2507 | $1.77 | $1.5 |
| Balanced workload | 1M input + 1M output | MiniMax M2 | $1.25 | $1.64 |
| Output-heavy chatbot | 1M input + 5M output | MiniMax M2 | $5.25 | $7.62 |
MiniMax M2 is $0.5 cheaper per 1M output tokens (33.1% lower; 1.5x difference).
Qwen3 235B A22B Thinking 2507 has 57.34K more context (1.28x larger).
MiniMax M2 is $0.14 cheaper on the standard workload (15.8% lower).
Estimate your workload cost
Your Workload Cost
This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.
Quick Decision
Qwen3 235B A22B Thinking 2507 has the lower input price; MiniMax M2 has the lower output price; Qwen3 235B A22B Thinking 2507 offers the larger context window. For the 1M input plus 500K output sample, MiniMax M2 is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $0.76 for MiniMax M2 and $0.9 for Qwen3 235B A22B Thinking 2507.
Choose MiniMax M2 when you care most about lower output-token price.
Choose Qwen3 235B A22B Thinking 2507 when you care most about lower input-token price, and larger context window.
- On the standard 1M input plus 500K output workload, MiniMax M2 is estimated at $0.76 vs $0.9 for Qwen3 235B A22B Thinking 2507, saving $0.14 (15.8% lower).
- MiniMax M2 is $0.14 cheaper on the standard workload (15.8% lower).
- Qwen3 235B A22B Thinking 2507 is $0.11 cheaper per 1M input tokens (41.4% lower; 1.71x difference).
- MiniMax M2 is $0.5 cheaper per 1M output tokens (33.1% lower; 1.5x difference).
- Qwen3 235B A22B Thinking 2507 has 57.34K more context (1.28x larger).
| Feature | MiniMax M2 (MiniMax) | Qwen3 235B A22B Thinking 2507 (Qwen) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.255 | $0.1495 |
| Completion Price per 1M tokens | $1 | $1.495 |
| Sample Workload Cost 1M input + 500K output | $0.76 | $0.9 |
| Context Window | 204.8K | 262.14K |
| Release Date | ||
| Popularity | #62 | #133 |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | MiniMax M2 | On the standard 1M input plus 500K output workload, MiniMax M2 is estimated at $0.76 vs $0.9 for Qwen3 235B A22B Thinking 2507, saving $0.14 (15.8% lower). |
| High-volume input processing | Qwen3 235B A22B Thinking 2507 | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | MiniMax M2 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Qwen3 235B A22B Thinking 2507 | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- MiniMax M2.5 (free) can replace MiniMax M2 when lower sample workload cost matters most: $0.
- MiniMax M2.5 can replace MiniMax M2 when lower sample workload cost matters most: $0.72.
- MiniMax-01 can replace MiniMax M2 when lower sample workload cost matters most: $0.75.
- Qwen3 Next 80B A3B Instruct (free) can replace Qwen3 235B A22B Thinking 2507 when lower sample workload cost matters most: $0.
- Llama 4 Scout offers 10M context with $0.23 sample workload cost.
- Owl Alpha offers 1.05M context with $0 sample workload cost.
- DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
- DeepSeek V4 Pro offers 1.05M context with $0.87 sample workload cost.
- DeepSeek V4 Flash · DeepSeek · #1
- Hy3 preview · Tencent · #2
- Claude Opus 4.7 · Anthropic · #3
- Claude Sonnet 4.6 · Anthropic · #4
Cheaper alternatives
Review low-cost models sorted by a standard 1M input plus 500K output workload.
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Open Qwen modelsMiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...
Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...