DeepSeek V4 Flash Vision Exp vs Ling 3.1 Flash vs Mercury 2.5
DeepSeek V4 Flash Vision Exp comes out ahead, 65 to 55 and 50 on our weighted score, and it is the cheaper option too.
- Our pick
DeepSeekDeepSeek V4 Flash Vision Exp
65/100- ECI—
- Price$0.216 / $0.647
- Context1M
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
DeepSeek V4 Flash Vision Exp is our pick
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 65/100 against Mercury 2.5 (55) and Ling 3.1 Flash (50). It leads on inputs & features and context window. 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 · Mercury 2.5 $0.338 · Ling 3.1 Flash $0.45 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp 1,000,000 · Ling 3.1 Flash 262,144 · Mercury 2.5 260,000 tokens
- Widest inputsDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp: Text, Images · Ling 3.1 Flash: Text · Mercury 2.5: Text
- Self-hostingDeepSeek V4 Flash Vision Exp and Ling 3.1 FlashPublishes downloadable weights (MIT)
| Measure | Weight | DeepSeek V4 Flash Vision Exp | Ling 3.1 Flash | Mercury 2.5 |
|---|---|---|---|---|
| Price | 50% | 70 | 64 | 69 |
| Inputs & features | 30% | 60 | 35 | 45 |
| Context window | 20% | 60 | 37 | 36 |
| Overall | 100% | 65/100 | 50/100 | 55/100 |
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 | DeepSeek V4 Flash Vision ExpDeepSeek | Ling 3.1 FlashinclusionAI | Mercury 2.5Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.216 | $0.30 | $0.20 (best) |
| Output | $0.647 (best) | $0.90 | $0.75 |
| Cached input | — | — | $0.02 |
| Blended (3:1) | $0.323 (best) | $0.45 | $0.338 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 14 providers | Median of 5 providers | Official Inception API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 262,144 tokens | 260,000 tokens |
| Max output | 384,000 tokens (best) | 32,768 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenMIT | Open | Proprietary |
| API model ID | — | — | mercury-2.5 |
| API providers | 15 (best) | 7 | 6 |
| Released | Aug 21, 2026 | Sep 29, 2026 | Sep 8, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek V4 Flash Vision Exp$3.45
- Ling 3.1 Flash$4.80
- Mercury 2.5$3.50
Which should you choose?
Which is better: DeepSeek V4 Flash Vision Exp, Ling 3.1 Flash or Mercury 2.5?
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 65/100 against Mercury 2.5 (55) and Ling 3.1 Flash (50). It leads on inputs & features and context window. 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, DeepSeek V4 Flash Vision Exp, Ling 3.1 Flash or Mercury 2.5?
DeepSeek V4 Flash Vision Exp is cheaper at $0.216 input / $0.647 output per million tokens (median across 14 API providers). Mercury 2.5 costs $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). 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 $0.338 for Mercury 2.5 (1× as much) and $0.45 for Ling 3.1 Flash (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek V4 Flash Vision Exp has not been scored yet, Ling 3.1 Flash has not been scored yet and Mercury 2.5 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4 Flash Vision Exp, Ling 3.1 Flash and Mercury 2.5 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?
DeepSeek V4 Flash Vision Exp has the largest context window at 1,000,000 tokens, against 262,144 for Ling 3.1 Flash and 260,000 for Mercury 2.5. Maximum output per response: DeepSeek V4 Flash Vision Exp up to 384,000, Ling 3.1 Flash up to 32,768, Mercury 2.5 up to 65,536 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4 Flash Vision Exp accepts text and images; Ling 3.1 Flash accepts text; Mercury 2.5 accepts text. DeepSeek V4 Flash Vision Exp handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Flash Vision Exp and Ling 3.1 Flash publishes its weights (MIT) and can be self-hosted; Mercury 2.5 is proprietary.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 2026. Mercury 2.5 came out Sep 8, 2026; DeepSeek V4 Flash Vision Exp came out Aug 21, 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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