Ling 3.1 Flash vs Mercury 2.5 vs Beam 1.0 501B A23B
Too close to call on our weighted score (Mercury 2.5 55, Beam 1.0 501B A23B 54, Ling 3.1 Flash 50). The right pick depends on what you value most.
inclusionAILing 3.1 Flash
50/100- ECI—
- Price$0.30 / $0.90
- Context262K
InceptionMercury 2.5
55/100- ECI—
- Price$0.20 / $0.75
- Context260K
Reflection AIBeam 1.0 501B A23B
54/100- ECI—
- Price$0.30 / $1.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 1 points (Mercury 2.5 55/100, Beam 1.0 501B A23B 54/100, Ling 3.1 Flash 50/100), so choose by what matters most for your work: Mercury 2.5 on price and Beam 1.0 501B A23B for long inputs. 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 priceMercury 2.5Mercury 2.5 $0.338 · 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 · Ling 3.1 Flash 262,144 · Mercury 2.5 260,000 tokens
- Widest inputsSame inputsLing 3.1 Flash: Text · Mercury 2.5: Text · Beam 1.0 501B A23B: Text
- Self-hostingLing 3.1 Flash and Beam 1.0 501B A23BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Ling 3.1 Flash | Mercury 2.5 | Beam 1.0 501B A23B |
|---|---|---|---|---|
| Price | 50% | 64 | 69 | 62 |
| Inputs & features | 30% | 35 | 45 | 35 |
| Context window | 20% | 37 | 36 | 61 |
| Overall | 100% | 50/100 | 55/100 | 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 | Mercury 2.5Inception | 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 | $0.75 (best) | $1.00 |
| Cached input | — | $0.02 | — |
| Blended (3:1) | $0.45 | $0.338 (best) | $0.475 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Official Inception API | Median of 7 providers |
| Limits | |||
| Context window | 262,144 tokens | 260,000 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 65,536 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 | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache-2.0 |
| API model ID | — | mercury-2.5 | — |
| API providers | 7 | 6 | 12 (best) |
| Released | Sep 29, 2026 | Sep 8, 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
- Mercury 2.5$3.50
- Beam 1.0 501B A23B$5.00
Which should you choose?
Which is better: Ling 3.1 Flash, Mercury 2.5 or Beam 1.0 501B A23B?
It is close. Our weighted score puts them within 1 points (Mercury 2.5 55/100, Beam 1.0 501B A23B 54/100, Ling 3.1 Flash 50/100), so choose by what matters most for your work: Mercury 2.5 on price and Beam 1.0 501B A23B for long inputs. 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, Mercury 2.5 or Beam 1.0 501B A23B?
Mercury 2.5 is cheaper at $0.20 input / $0.75 output per million tokens (official Inception 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 $0.338 per million tokens for Mercury 2.5 versus $0.45 for Ling 3.1 Flash (1.3× as much) and $0.475 for Beam 1.0 501B A23B (1.4× 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, Mercury 2.5 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, Mercury 2.5 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 262,144 for Ling 3.1 Flash and 260,000 for Mercury 2.5. Maximum output per response: Ling 3.1 Flash up to 32,768, Mercury 2.5 up to 65,536, Beam 1.0 501B A23B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling 3.1 Flash accepts text; Mercury 2.5 accepts text; Beam 1.0 501B A23B accepts text. They handle the same number of input types.
Are any of these open source?
Ling 3.1 Flash and Beam 1.0 501B A23B publishes its weights (Apache-2.0) and can be self-hosted; Mercury 2.5 is proprietary.
Which is newer?
Beam 1.0 501B A23B is the newest, released Oct 5, 2026. Ling 3.1 Flash came out Sep 29, 2026; Mercury 2.5 came out Sep 8, 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.
