Beam 1.0 501B A23B vs MiniMax-M3.1-Flash vs Ling 3.1 Flash
MiniMax-M3.1-Flash comes out ahead, 70 to 54 and 50 on our weighted score, and it is the cheaper option too.
Reflection AIBeam 1.0 501B A23B
54/100- ECI—
- Price$0.30 / $1.00
- Context1.05M
- Our pick
MiniMaxMiniMax-M3.1-Flash
70/100- ECI—
- Price$0.15 / $0.60
- Context1M
inclusionAILing 3.1 Flash
50/100- ECI—
- Price$0.30 / $0.90
- Context262K
MiniMax-M3.1-Flash is our pick
MiniMax-M3.1-Flash is the better all-round choice, scoring 70/100 against Beam 1.0 501B A23B (54) and Ling 3.1 Flash (50). It leads on price and inputs & features. 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-M3.1-FlashMiniMax-M3.1-Flash $0.263 · Ling 3.1 Flash $0.45 · Beam 1.0 501B A23B $0.475 per 1M tokens (3:1 blend)
- Longest contextBeam 1.0 501B A23BBeam 1.0 501B A23B 1,048,576 · MiniMax-M3.1-Flash 1,000,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsMiniMax-M3.1-FlashBeam 1.0 501B A23B: Text · MiniMax-M3.1-Flash: Text, Images, Video · Ling 3.1 Flash: Text
- Self-hostingBeam 1.0 501B A23B and Ling 3.1 FlashPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Beam 1.0 501B A23B | MiniMax-M3.1-Flash | Ling 3.1 Flash |
|---|---|---|---|---|
| Price | 50% | 62 | 74 | 64 |
| Inputs & features | 30% | 35 | 70 | 35 |
| Context window | 20% | 61 | 60 | 37 |
| Overall | 100% | 54/100 | 70/100 | 50/100 |
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 | Beam 1.0 501B A23BReflection AI | MiniMax-M3.1-FlashMiniMax | Ling 3.1 FlashinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.15 (best) | $0.30 |
| Output | $1.00 | $0.60 (best) | $0.90 |
| Cached input | — | $0.015 | — |
| Blended (3:1) | $0.475 | $0.263 (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official MiniMax (minimax.io) API | Median of 5 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,000,000 tokens | 262,144 tokens |
| Max output | 131,072 tokens | 512,000 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Open |
| API model ID | — | MiniMax-M3.1-Flash | — |
| API providers | 12 | 25 (best) | 7 |
| Released | Oct 5, 2026 | Oct 19, 2026 | Sep 29, 2026 |
| Knowledge cutoff | Jun 30, 2026 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Beam 1.0 501B A23B$5.00
- MiniMax-M3.1-Flash$2.70
- Ling 3.1 Flash$4.80
Which should you choose?
Which is better: Beam 1.0 501B A23B, MiniMax-M3.1-Flash or Ling 3.1 Flash?
MiniMax-M3.1-Flash is the better all-round choice, scoring 70/100 against Beam 1.0 501B A23B (54) and Ling 3.1 Flash (50). It leads on price and inputs & features. 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, Beam 1.0 501B A23B, MiniMax-M3.1-Flash or Ling 3.1 Flash?
MiniMax-M3.1-Flash is cheaper at $0.15 input / $0.60 output per million tokens (official MiniMax (minimax.io) API price). Ling 3.1 Flash costs $0.30 input / $0.90 output per million tokens (median across 5 API providers); Beam 1.0 501B A23B costs $0.30 input / $1.00 output per million tokens (median across 7 API providers). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for MiniMax-M3.1-Flash versus $0.45 for Ling 3.1 Flash (1.7× as much) and $0.475 for Beam 1.0 501B A23B (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Beam 1.0 501B A23B has not been scored yet, MiniMax-M3.1-Flash has not been scored yet and Ling 3.1 Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Beam 1.0 501B A23B, MiniMax-M3.1-Flash 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?
Beam 1.0 501B A23B has the largest context window at 1,048,576 tokens, against 1,000,000 for MiniMax-M3.1-Flash and 262,144 for Ling 3.1 Flash. Maximum output per response: Beam 1.0 501B A23B up to 131,072, MiniMax-M3.1-Flash up to 512,000, Ling 3.1 Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Beam 1.0 501B A23B accepts text; MiniMax-M3.1-Flash accepts text, images and video; Ling 3.1 Flash accepts text. MiniMax-M3.1-Flash handles the widest range of inputs.
Are any of these open source?
Beam 1.0 501B A23B and Ling 3.1 Flash publishes its weights (Apache-2.0) and can be self-hosted; MiniMax-M3.1-Flash is proprietary.
Which is newer?
MiniMax-M3.1-Flash is the newest, released Oct 19, 2026. Beam 1.0 501B A23B came out Oct 5, 2026; Ling 3.1 Flash came out Sep 29, 2026. Knowledge cutoff: Beam 1.0 501B A23B Jun 30, 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 Nov 2, 2026.
