MiniMax-M3 vs GPT-6 Luna vs Qwen3.7 Plus
GPT-6 Luna comes out ahead, 86 to 78 and 73 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M3
73/100- ECI147.0
- Price$0.30 / $1.20
- Context1.05M
- Our pick
OpenAIGPT-6 Luna
86/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
Alibaba (Qwen)Qwen3.7 Plus
78/100- ECI147.4
- Price$0.40 / $1.60
- Context1M
GPT-6 Luna is our pick
GPT-6 Luna is the better all-round choice, scoring 86/100 against Qwen3.7 Plus (78) and MiniMax-M3 (73). It leads on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because GPT-6 Luna has no Capabilities Index score yet.
- CapabilityGPT-6 LunaShared benchmarks: GPT-6 Luna 94.7% · Qwen3.7 Plus 90.6% · MiniMax-M3 81.0%
- Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · MiniMax-M3 $0.525 · Qwen3.7 Plus $0.70 per 1M tokens (3:1 blend)
- Longest contextGPT-6 Luna and MiniMax-M3GPT-6 Luna 1,050,000 · MiniMax-M3 1,048,576 · Qwen3.7 Plus 1,000,000 tokens
- Widest inputsSame inputsMiniMax-M3: Text, Images, Video · GPT-6 Luna: Text, Images, PDFs · Qwen3.7 Plus: Text, Images, Video
- Self-hostingMiniMax-M3Publishes downloadable weights
| Measure | Weight | MiniMax-M3 | GPT-6 Luna | Qwen3.7 Plus |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 81 | 95 | 91 |
| Price | 25% | 63 | 83 | 57 |
| Inputs & features | 15% | 70 | 80 | 80 |
| Context window | 10% | 61 | 61 | 60 |
| Overall | 100% | 73/100 | 86/100 | 78/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M3MiniMax | GPT-6 LunaOpenAI | Qwen3.7 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 147.0 | — | 147.4 (best) |
| ECI rank | #62 of 148 | — | #61 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 90.9% (best) | 90.5% | 87.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 79.0% (best) | 34.4% |
| OTIS Mock AIME 2024–2025Competition mathematics | 71.1% | 98.9% (best) | 93.3% |
| SimpleQA VerifiedShort factual questions | — | 41.4% | — |
| Price per million tokens | |||
| Input | $0.30 | $0.10 (best) | $0.40 |
| Output | $1.20 | $0.50 (best) | $1.60 |
| Cached input | $0.06 | $0.01 (best) | $0.04 |
| Blended (3:1) | $0.525 | $0.20 (best) | $0.70 |
| Long-context rate | Over 512K: $0.60 / $2.40 | Over 272K: $0.20 / $0.75 | Over 256K: $1.20 / $4.80 |
| Price source | Official MiniMax (minimax.io) API | Official OpenAI API | Official Alibaba API |
| Limits | |||
| Context window | 1,048,576 tokens | 1,050,000 tokens (best) | 1,000,000 tokens |
| Max output | 512,000 tokens (best) | 128,000 tokens | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | MiniMax-M3 | gpt-6-luna | qwen3.7-plus |
| API providers | 42 (best) | 24 | 25 |
| Released | Jun 1, 2026 | Sep 22, 2026 | Jun 2, 2026 |
| Knowledge cutoff | — | May 18, 2026 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M3$5.40
- GPT-6 Luna$2.00
- Qwen3.7 Plus$7.20
Which should you choose?
Which is better: MiniMax-M3, GPT-6 Luna or Qwen3.7 Plus?
GPT-6 Luna is the better all-round choice, scoring 86/100 against Qwen3.7 Plus (78) and MiniMax-M3 (73). It leads on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because GPT-6 Luna has no Capabilities Index score yet.
Which is cheaper, MiniMax-M3, GPT-6 Luna or Qwen3.7 Plus?
GPT-6 Luna is cheaper at $0.10 input / $0.50 output per million tokens (official OpenAI API price). MiniMax-M3 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price); Qwen3.7 Plus costs $0.40 input / $1.60 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.20 per million tokens for GPT-6 Luna versus $0.525 for MiniMax-M3 (2.6× as much) and $0.70 for Qwen3.7 Plus (3.5× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): GPT-6 Luna 94.7%, Qwen3.7 Plus 90.6% and MiniMax-M3 81.0%. On individual benchmarks: GPQA Diamond — MiniMax-M3 90.9%, GPT-6 Luna 90.5%, Qwen3.7 Plus 87.9%; OTIS Mock AIME 2024–2025 — GPT-6 Luna 98.9%, Qwen3.7 Plus 93.3%, MiniMax-M3 71.1%.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M3, GPT-6 Luna and Qwen3.7 Plus yet, so there is no like-for-like coding score. On overall capability, GPT-6 Luna leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-6 Luna and MiniMax-M3 have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for Qwen3.7 Plus. Maximum output per response: MiniMax-M3 up to 512,000, GPT-6 Luna up to 128,000, Qwen3.7 Plus up to 64,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M3 accepts text, images and video; GPT-6 Luna accepts text, images and PDFs; Qwen3.7 Plus accepts text, images and video. They handle the same number of input types.
Are any of these open source?
MiniMax-M3 publishes its weights and can be self-hosted; GPT-6 Luna and Qwen3.7 Plus is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. Qwen3.7 Plus came out Jun 2, 2026; MiniMax-M3 came out Jun 1, 2026. Knowledge cutoff: GPT-6 Luna May 18, 2026, Qwen3.7 Plus Apr 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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