MiniMax-M3.1-Flash-Preview vs MiniMax-M3 vs Ling 3.1 Flash
Too close to call on our weighted score (MiniMax-M3 66, MiniMax-M3.1-Flash-Preview 66, Ling 3.1 Flash 36). The right pick depends on what you value most.
MiniMaxMiniMax-M3.1-Flash-Preview
66/100- ECI—
- Price—
- Context1M
- MiniMax
MiniMax-M3
66/100- ECI147.0
- Price$0.30 / $1.20
- Context1.05M
inclusionAILing 3.1 Flash
36/100- ECI—
- Price$0.075 / $0.22
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (MiniMax-M3 66/100, MiniMax-M3.1-Flash-Preview 66/100, Ling 3.1 Flash 36/100), so choose by what matters most for your work: Ling 3.1 Flash on price and MiniMax-M3 for long inputs. The score weighs inputs & features 60%, context window 40%. 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 priceLing 3.1 FlashLing 3.1 Flash $0.111 · MiniMax-M3 $0.525 per 1M tokens (3:1 blend) · MiniMax-M3.1-Flash-Preview unpriced
- Longest contextMiniMax-M3MiniMax-M3 1,048,576 · MiniMax-M3.1-Flash-Preview 1,000,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsMiniMax-M3.1-Flash-Preview and MiniMax-M3MiniMax-M3.1-Flash-Preview: Text, Images, Video · MiniMax-M3: Text, Images, Video · Ling 3.1 Flash: Text
- Self-hostingMiniMax-M3Publishes downloadable weights
| Measure | Weight | MiniMax-M3.1-Flash-Preview | MiniMax-M3 | Ling 3.1 Flash |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 70 | 35 |
| Context window | 40% | 60 | 61 | 37 |
| Overall | 100% | 66/100 | 66/100 | 36/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M3.1-Flash-PreviewMiniMax | MiniMax-M3MiniMax | Ling 3.1 FlashinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 147.0 | — |
| ECI rank | — | #62 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 90.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 71.1% | — |
| Price per million tokens | |||
| Input | — | $0.30 | $0.075 (best) |
| Output | — | $1.20 | $0.22 (best) |
| Cached input | — | $0.06 | — |
| Blended (3:1) | — | $0.525 | $0.111 (best) |
| Long-context rate | — | Over 512K: $0.60 / $2.40 | Same rate |
| Price source | — | Official MiniMax (minimax.io) API | Median of 1 providers |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 512,000 tokens (best) | 512,000 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | MiniMax-M3 | — |
| API providers | — | 42 (best) | 3 |
| Released | Sep 27, 2026 | Jun 1, 2026 | Sep 29, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M3.1-Flash-Preview—
- MiniMax-M3$5.40
- Ling 3.1 Flash$1.19
Which should you choose?
Which is better: MiniMax-M3.1-Flash-Preview, MiniMax-M3 or Ling 3.1 Flash?
It is close. Our weighted score puts them within a point (MiniMax-M3 66/100, MiniMax-M3.1-Flash-Preview 66/100, Ling 3.1 Flash 36/100), so choose by what matters most for your work: Ling 3.1 Flash on price and MiniMax-M3 for long inputs. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, MiniMax-M3.1-Flash-Preview, MiniMax-M3 or Ling 3.1 Flash?
Ling 3.1 Flash is cheaper at $0.075 input / $0.22 output per million tokens (median across 1 API provider). MiniMax-M3 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.111 per million tokens for Ling 3.1 Flash versus $0.525 for MiniMax-M3 (4.7× as much). MiniMax-M3.1-Flash-Preview has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M3.1-Flash-Preview has not been scored yet, MiniMax-M3 has an ECI of 147.0 and Ling 3.1 Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M3.1-Flash-Preview, MiniMax-M3 and Ling 3.1 Flash 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?
MiniMax-M3 has the largest context window at 1,048,576 tokens, against 1,000,000 for MiniMax-M3.1-Flash-Preview and 262,144 for Ling 3.1 Flash. Maximum output per response: MiniMax-M3.1-Flash-Preview up to 512,000, MiniMax-M3 up to 512,000, Ling 3.1 Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M3.1-Flash-Preview accepts text, images and video; MiniMax-M3 accepts text, images and video; Ling 3.1 Flash accepts text. MiniMax-M3.1-Flash-Preview handles the widest range of inputs.
Are any of these open source?
MiniMax-M3 publishes its weights and can be self-hosted; MiniMax-M3.1-Flash-Preview and Ling 3.1 Flash is proprietary.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 2026. MiniMax-M3.1-Flash-Preview came out Sep 27, 2026; MiniMax-M3 came out Jun 1, 2026.
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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