LongCat-2.5-Preview vs Beam 1.0 501B A23B vs MiMo-V2.6-Pro
MiMo-V2.6-Pro comes out ahead, 69 to 60 and 54 on our weighted score, though Beam 1.0 501B A23B is 13% cheaper per token.
Meituan (LongCat)LongCat-2.5-Preview
60/100- ECI—
- Price$0.30 / $1.20
- Context1M
Reflection AIBeam 1.0 501B A23B
54/100- ECI—
- Price$0.30 / $1.00
- Context1.05M
- Our pick
XiaomiMiMo-V2.6-Pro
69/100- ECI—
- Price$0.435 / $0.87
- Context1.05M
MiMo-V2.6-Pro is our pick
MiMo-V2.6-Pro is the better all-round choice, scoring 69/100 against LongCat-2.5-Preview (60) and Beam 1.0 501B A23B (54). It leads on inputs & features. Beam 1.0 501B A23B 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 priceBeam 1.0 501B A23BBeam 1.0 501B A23B $0.475 · LongCat-2.5-Preview $0.525 · MiMo-V2.6-Pro $0.544 per 1M tokens (3:1 blend)
- Longest contextBeam 1.0 501B A23B and MiMo-V2.6-ProBeam 1.0 501B A23B 1,048,576 · MiMo-V2.6-Pro 1,048,576 · LongCat-2.5-Preview 1,000,000 tokens
- Widest inputsMiMo-V2.6-ProLongCat-2.5-Preview: Text, Images · Beam 1.0 501B A23B: Text · MiMo-V2.6-Pro: Text, Images, Audio, Video
- Self-hostingBeam 1.0 501B A23B and MiMo-V2.6-ProPublishes downloadable weights (Apache-2.0)
| Measure | Weight | LongCat-2.5-Preview | Beam 1.0 501B A23B | MiMo-V2.6-Pro |
|---|---|---|---|---|
| Price | 50% | 60 | 62 | 60 |
| Inputs & features | 30% | 60 | 35 | 90 |
| Context window | 20% | 60 | 61 | 61 |
| Overall | 100% | 60/100 | 54/100 | 69/100 |
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 | LongCat-2.5-PreviewMeituan (LongCat) | Beam 1.0 501B A23BReflection AI | MiMo-V2.6-ProXiaomi |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.30 (best) | $0.435 |
| Output | $1.20 | $1.00 | $0.87 (best) |
| Cached input | $0.006 | — | $0.0036 (best) |
| Blended (3:1) | $0.525 | $0.475 (best) | $0.544 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official LongCat API | Median of 7 providers | Official Xiaomi API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 1,048,576 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | Open |
| API model ID | LongCat-2.5-Preview | — | mimo-v2.6-pro |
| API providers | 4 | 12 | 21 (best) |
| Released | Sep 25, 2026 | Oct 5, 2026 | Sep 22, 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.
- LongCat-2.5-Preview$5.40
- Beam 1.0 501B A23B$5.00
- MiMo-V2.6-Pro$6.09
Which should you choose?
Which is better: LongCat-2.5-Preview, Beam 1.0 501B A23B or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is the better all-round choice, scoring 69/100 against LongCat-2.5-Preview (60) and Beam 1.0 501B A23B (54). It leads on inputs & features. Beam 1.0 501B A23B 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, LongCat-2.5-Preview, Beam 1.0 501B A23B or MiMo-V2.6-Pro?
Beam 1.0 501B A23B is cheaper at $0.30 input / $1.00 output per million tokens (median across 7 API providers). LongCat-2.5-Preview costs $0.30 input / $1.20 output per million tokens (official LongCat API price); MiMo-V2.6-Pro costs $0.435 input / $0.87 output per million tokens (official Xiaomi API price). At a typical mix of three input tokens to one output token, that is $0.475 per million tokens for Beam 1.0 501B A23B versus $0.525 for LongCat-2.5-Preview (1.1× as much) and $0.544 for MiMo-V2.6-Pro (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. LongCat-2.5-Preview has not been scored yet, Beam 1.0 501B A23B has not been scored yet and MiMo-V2.6-Pro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for LongCat-2.5-Preview, Beam 1.0 501B A23B and MiMo-V2.6-Pro 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 and MiMo-V2.6-Pro have the largest context windows (1,048,576 and 1,048,576 tokens), against 1,000,000 for LongCat-2.5-Preview. Maximum output per response: LongCat-2.5-Preview up to 131,072, Beam 1.0 501B A23B up to 131,072, MiMo-V2.6-Pro up to 131,072 tokens.
Which can read images, PDFs, audio or video?
LongCat-2.5-Preview accepts text and images; Beam 1.0 501B A23B accepts text; MiMo-V2.6-Pro accepts text, images, audio and video. MiMo-V2.6-Pro handles the widest range of inputs.
Are any of these open source?
Beam 1.0 501B A23B and MiMo-V2.6-Pro publishes its weights (Apache-2.0) and can be self-hosted; LongCat-2.5-Preview is proprietary.
Which is newer?
Beam 1.0 501B A23B is the newest, released Oct 5, 2026. LongCat-2.5-Preview came out Sep 25, 2026; MiMo-V2.6-Pro came out Sep 22, 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.
