Beam 1.0 501B A23B vs Ling 3.1 Flash vs Nemotron 3.5 Super 120B A12B
Nemotron 3.5 Super 120B A12B comes out ahead, 59 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
inclusionAILing 3.1 Flash
50/100- ECI—
- Price$0.30 / $0.90
- Context262K
- Our pick
NVIDIANemotron 3.5 Super 120B A12B
59/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
Nemotron 3.5 Super 120B A12B is our pick
Nemotron 3.5 Super 120B A12B is the better all-round choice, scoring 59/100 against Beam 1.0 501B A23B (54) and Ling 3.1 Flash (50). It leads 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 priceNemotron 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B $0.275 · Ling 3.1 Flash $0.45 · Beam 1.0 501B A23B $0.475 per 1M tokens (3:1 blend)
- Longest contextBeam 1.0 501B A23B and Nemotron 3.5 Super 120B A12BBeam 1.0 501B A23B 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 · Ling 3.1 Flash 262,144 tokens
- Widest inputsSame inputsBeam 1.0 501B A23B: Text · Ling 3.1 Flash: Text · Nemotron 3.5 Super 120B A12B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Beam 1.0 501B A23B | Ling 3.1 Flash | Nemotron 3.5 Super 120B A12B |
|---|---|---|---|---|
| Price | 50% | 62 | 64 | 73 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 61 | 37 | 61 |
| Overall | 100% | 54/100 | 50/100 | 59/100 |
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 | Ling 3.1 FlashinclusionAI | Nemotron 3.5 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.30 | $0.20 (best) |
| Output | $1.00 | $0.90 | $0.50 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.475 | $0.45 | $0.275 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 5 providers | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 131,072 tokens (best) | 32,768 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Open |
| API model ID | — | — | — |
| API providers | 12 (best) | 7 | 1 |
| Released | Oct 5, 2026 | Sep 29, 2026 | Oct 15, 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
- Ling 3.1 Flash$4.80
- Nemotron 3.5 Super 120B A12B$3.00
Which should you choose?
Which is better: Beam 1.0 501B A23B, Ling 3.1 Flash or Nemotron 3.5 Super 120B A12B?
Nemotron 3.5 Super 120B A12B is the better all-round choice, scoring 59/100 against Beam 1.0 501B A23B (54) and Ling 3.1 Flash (50). It leads 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, Beam 1.0 501B A23B, Ling 3.1 Flash or Nemotron 3.5 Super 120B A12B?
Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). 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 $0.275 per million tokens for Nemotron 3.5 Super 120B A12B versus $0.45 for Ling 3.1 Flash (1.6× as much) and $0.475 for Beam 1.0 501B A23B (1.7× 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, Ling 3.1 Flash has not been scored yet and Nemotron 3.5 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Beam 1.0 501B A23B, Ling 3.1 Flash and Nemotron 3.5 Super 120B A12B 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 Nemotron 3.5 Super 120B A12B have the largest context windows (1,048,576 and 1,048,576 tokens), against 262,144 for Ling 3.1 Flash. Maximum output per response: Beam 1.0 501B A23B up to 131,072, Ling 3.1 Flash up to 32,768, Nemotron 3.5 Super 120B A12B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Beam 1.0 501B A23B accepts text; Ling 3.1 Flash accepts text; Nemotron 3.5 Super 120B A12B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 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.
