Ling 3.1 Flash vs DeepSeek V4 Flash Vision Exp vs Beam 1.0 501B A23B
DeepSeek V4 Flash Vision Exp comes out ahead, 65 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
DeepSeekDeepSeek V4 Flash Vision Exp
65/100- ECI—
- Price$0.216 / $0.647
- Context1M
Reflection AIBeam 1.0 501B A23B
54/100- ECI—
- Price$0.30 / $1.00
- Context1.05M
DeepSeek V4 Flash Vision Exp is our pick
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 65/100 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 priceDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp $0.323 · 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 · DeepSeek V4 Flash Vision Exp 1,000,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsDeepSeek V4 Flash Vision ExpLing 3.1 Flash: Text · DeepSeek V4 Flash Vision Exp: Text, Images · 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 | DeepSeek V4 Flash Vision Exp | Beam 1.0 501B A23B |
|---|---|---|---|---|
| Price | 50% | 64 | 70 | 62 |
| Inputs & features | 30% | 35 | 60 | 35 |
| Context window | 20% | 37 | 60 | 61 |
| Overall | 100% | 50/100 | 65/100 | 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 | DeepSeek V4 Flash Vision ExpDeepSeek | Beam 1.0 501B A23BReflection AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.216 (best) | $0.30 |
| Output | $0.90 | $0.647 (best) | $1.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 | $0.323 (best) | $0.475 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 14 providers | Median of 7 providers |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 384,000 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | 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 | OpenMIT | OpenApache-2.0 |
| API model ID | — | — | — |
| API providers | 7 | 15 (best) | 12 |
| Released | Sep 29, 2026 | Aug 21, 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
- DeepSeek V4 Flash Vision Exp$3.45
- Beam 1.0 501B A23B$5.00
Which should you choose?
Which is better: Ling 3.1 Flash, DeepSeek V4 Flash Vision Exp or Beam 1.0 501B A23B?
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 65/100 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, Ling 3.1 Flash, DeepSeek V4 Flash Vision Exp or Beam 1.0 501B A23B?
DeepSeek V4 Flash Vision Exp is cheaper at $0.216 input / $0.647 output per million tokens (median across 14 API providers). 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 $0.323 per million tokens for DeepSeek V4 Flash Vision Exp versus $0.45 for Ling 3.1 Flash (1.4× as much) and $0.475 for Beam 1.0 501B A23B (1.5× 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, DeepSeek V4 Flash Vision Exp 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, DeepSeek V4 Flash Vision Exp 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?
Beam 1.0 501B A23B has the largest context window at 1,048,576 tokens, against 1,000,000 for DeepSeek V4 Flash Vision Exp and 262,144 for Ling 3.1 Flash. Maximum output per response: Ling 3.1 Flash up to 32,768, DeepSeek V4 Flash Vision Exp up to 384,000, Beam 1.0 501B A23B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling 3.1 Flash accepts text; DeepSeek V4 Flash Vision Exp accepts text and images; Beam 1.0 501B A23B accepts text. DeepSeek V4 Flash Vision Exp handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT and Apache-2.0), so you can self-host them.
Which is newer?
Beam 1.0 501B A23B is the newest, released Oct 5, 2026. Ling 3.1 Flash came out Sep 29, 2026; DeepSeek V4 Flash Vision Exp came out Aug 21, 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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