GPT-5.1 Codex mini vs MiniMax-M2 vs Qwen3 VL 235B A22B Instruct
GPT-5.1 Codex mini comes out ahead, 59 to 53 and 48 on our weighted score, though MiniMax-M2 is 24% cheaper per token.
- Our pick
OpenAIGPT-5.1 Codex mini
59/100- ECI—
- Price$0.25 / $2.00
- Context400K
MiniMaxMiniMax-M2
48/100- ECI—
- Price$0.30 / $1.20
- Context205K
Alibaba (Qwen)Qwen3 VL 235B A22B Instruct
53/100- ECI—
- Price$0.30 / $1.55
- Context131K
GPT-5.1 Codex mini is our pick
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Qwen3 VL 235B A22B Instruct (53) and MiniMax-M2 (48). It leads on inputs & features and context window. MiniMax-M2 wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMiniMax-M2MiniMax-M2 $0.525 · Qwen3 VL 235B A22B Instruct $0.613 · GPT-5.1 Codex mini $0.688 per 1M tokens (3:1 blend)
- Longest contextGPT-5.1 Codex miniGPT-5.1 Codex mini 400,000 · MiniMax-M2 204,800 · Qwen3 VL 235B A22B Instruct 131,072 tokens
- Widest inputsGPT-5.1 Codex mini and Qwen3 VL 235B A22B InstructGPT-5.1 Codex mini: Text, Images · MiniMax-M2: Text · Qwen3 VL 235B A22B Instruct: Text, Images
- Self-hostingMiniMax-M2 and Qwen3 VL 235B A22B InstructPublishes downloadable weights
| Measure | Weight | GPT-5.1 Codex mini | MiniMax-M2 | Qwen3 VL 235B A22B Instruct |
|---|---|---|---|---|
| Price | 50% | 58 | 63 | 60 |
| Inputs & features | 30% | 70 | 35 | 60 |
| Context window | 20% | 44 | 32 | 24 |
| Overall | 100% | 59/100 | 48/100 | 53/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.1 Codex miniOpenAI | MiniMax-M2MiniMax | Qwen3 VL 235B A22B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.30 | $0.30 |
| Output | $2.00 | $1.20 (best) | $1.55 |
| Cached input | — | — | — |
| Blended (3:1) | $0.688 | $0.525 (best) | $0.613 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official MiniMax (minimax.io) API | Median of 12 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 204,800 tokens | 131,072 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | MiniMax-M2 | — |
| API providers | 10 | 13 (best) | 12 |
| Released | Nov 13, 2025 | Oct 27, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Sep 30, 2024 | — | Mar 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.1 Codex mini$6.50
- MiniMax-M2$5.40
- Qwen3 VL 235B A22B Instruct$6.10
Which should you choose?
Which is better: GPT-5.1 Codex mini, MiniMax-M2 or Qwen3 VL 235B A22B Instruct?
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Qwen3 VL 235B A22B Instruct (53) and MiniMax-M2 (48). It leads on inputs & features and context window. MiniMax-M2 wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-5.1 Codex mini, MiniMax-M2 or Qwen3 VL 235B A22B Instruct?
MiniMax-M2 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Qwen3 VL 235B A22B Instruct costs $0.30 input / $1.55 output per million tokens (median across 12 API providers); GPT-5.1 Codex mini costs $0.25 input / $2.00 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M2 versus $0.613 for Qwen3 VL 235B A22B Instruct (1.2× as much) and $0.688 for GPT-5.1 Codex mini (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.1 Codex mini has not been scored yet, MiniMax-M2 has not been scored yet and Qwen3 VL 235B A22B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.1 Codex mini, MiniMax-M2 and Qwen3 VL 235B A22B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5.1 Codex mini has the largest context window at 400,000 tokens, against 204,800 for MiniMax-M2 and 131,072 for Qwen3 VL 235B A22B Instruct. Maximum output per response: GPT-5.1 Codex mini up to 128,000, MiniMax-M2 up to 131,072, Qwen3 VL 235B A22B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GPT-5.1 Codex mini accepts text and images; MiniMax-M2 accepts text; Qwen3 VL 235B A22B Instruct accepts text and images. GPT-5.1 Codex mini handles the widest range of inputs.
Are any of these open source?
MiniMax-M2 and Qwen3 VL 235B A22B Instruct publishes its weights and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
GPT-5.1 Codex mini is the newest, released Nov 13, 2025. MiniMax-M2 came out Oct 27, 2025; Qwen3 VL 235B A22B Instruct came out Sep 23, 2025. Knowledge cutoff: GPT-5.1 Codex mini Sep 30, 2024, Qwen3 VL 235B A22B Instruct Mar 31, 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.
