Ling 3.1 Flash vs Nemotron 3.5 Super 120B A12B vs Beam 1.0 501B A23B
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.
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
Reflection AIBeam 1.0 501B A23B
54/100- ECI—
- Price$0.30 / $1.00
- 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 contextNemotron 3.5 Super 120B A12B and Beam 1.0 501B A23BNemotron 3.5 Super 120B A12B 1,048,576 · Beam 1.0 501B A23B 1,048,576 · Ling 3.1 Flash 262,144 tokens
- Widest inputsSame inputsLing 3.1 Flash: Text · Nemotron 3.5 Super 120B A12B: Text · Beam 1.0 501B A23B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ling 3.1 Flash | Nemotron 3.5 Super 120B A12B | Beam 1.0 501B A23B |
|---|---|---|---|---|
| Price | 50% | 64 | 73 | 62 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 37 | 61 | 61 |
| Overall | 100% | 50/100 | 59/100 | 54/100 |
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 | Ling 3.1 FlashinclusionAI | Nemotron 3.5 Super 120B A12BNVIDIA | Beam 1.0 501B A23BReflection AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.20 (best) | $0.30 |
| Output | $0.90 | $0.50 (best) | $1.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 | $0.275 (best) | $0.475 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 1 providers | Median of 7 providers |
| Limits | |||
| Context window | 262,144 tokens | 1,048,576 tokens (best) | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens (best) | 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 | Open | Open | OpenApache-2.0 |
| API model ID | — | — | — |
| API providers | 7 | 1 | 12 (best) |
| Released | Sep 29, 2026 | Oct 15, 2026 | Oct 5, 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.
- Ling 3.1 Flash$4.80
- Nemotron 3.5 Super 120B A12B$3.00
- Beam 1.0 501B A23B$5.00
Which should you choose?
Which is better: Ling 3.1 Flash, Nemotron 3.5 Super 120B A12B or Beam 1.0 501B A23B?
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, Ling 3.1 Flash, Nemotron 3.5 Super 120B A12B or Beam 1.0 501B A23B?
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. Ling 3.1 Flash has not been scored yet, Nemotron 3.5 Super 120B A12B has not been scored yet and Beam 1.0 501B A23B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ling 3.1 Flash, Nemotron 3.5 Super 120B A12B and Beam 1.0 501B A23B 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?
Nemotron 3.5 Super 120B A12B and Beam 1.0 501B A23B 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: Ling 3.1 Flash up to 32,768, Nemotron 3.5 Super 120B A12B up to 131,072, Beam 1.0 501B A23B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling 3.1 Flash accepts text; Nemotron 3.5 Super 120B A12B accepts text; Beam 1.0 501B A23B 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.
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