Mercury 2.5 vs GPT-5.6 Cyber vs DeepSeek V4 Flash Vision Exp
DeepSeek V4 Flash Vision Exp comes out ahead, 65 to 55 and 30 on our weighted score, and it is the cheaper option too.
InceptionMercury 2.5
55/100- ECI—
- Price$0.20 / $0.75
- Context260K
OpenAIGPT-5.6 Cyber
30/100- ECI—
- Price$12.50 / $75.00
- Context400K
- Our pick
DeepSeekDeepSeek V4 Flash Vision Exp
65/100- ECI—
- Price$0.216 / $0.647
- Context1M
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 GPT-5.6 Cyber (30). It leads on context window. GPT-5.6 Cyber wins on 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 · Mercury 2.5 $0.338 · GPT-5.6 Cyber $28.13 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 Flash Vision ExpDeepSeek V4 Flash Vision Exp 1,000,000 · GPT-5.6 Cyber 400,000 · Mercury 2.5 260,000 tokens
- Widest inputsGPT-5.6 Cyber and DeepSeek V4 Flash Vision ExpMercury 2.5: Text · GPT-5.6 Cyber: Text, Images · DeepSeek V4 Flash Vision Exp: Text, Images
- Self-hostingDeepSeek V4 Flash Vision ExpPublishes downloadable weights (MIT)
| Measure | Weight | Mercury 2.5 | GPT-5.6 Cyber | DeepSeek V4 Flash Vision Exp |
|---|---|---|---|---|
| Price | 50% | 69 | 0 | 70 |
| Inputs & features | 30% | 45 | 70 | 60 |
| Context window | 20% | 36 | 44 | 60 |
| Overall | 100% | 55/100 | 30/100 | 65/100 |
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 | Mercury 2.5Inception | GPT-5.6 CyberOpenAI | DeepSeek V4 Flash Vision ExpDeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $12.50 | $0.216 |
| Output | $0.75 | $75.00 | $0.647 (best) |
| Cached input | $0.02 (best) | $1.25 | — |
| Blended (3:1) | $0.338 | $28.13 | $0.323 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official OpenAI API | Median of 14 providers |
| Limits | |||
| Context window | 260,000 tokens | 400,000 tokens | 1,000,000 tokens (best) |
| Max output | 65,536 tokens | 128,000 tokens | 384,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenMIT |
| API model ID | mercury-2.5 | gpt-daybreak-red-latest | — |
| API providers | 6 | 1 | 15 (best) |
| Released | Sep 8, 2026 | Aug 7, 2026 | Aug 21, 2026 |
| Knowledge cutoff | — | Feb 16, 2026 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mercury 2.5$3.50
- GPT-5.6 Cyber$275.00
- DeepSeek V4 Flash Vision Exp$3.45
Which should you choose?
Which is better: Mercury 2.5, GPT-5.6 Cyber or DeepSeek V4 Flash Vision Exp?
DeepSeek V4 Flash Vision Exp is the better all-round choice, scoring 65/100 against Mercury 2.5 (55) and GPT-5.6 Cyber (30). It leads on context window. GPT-5.6 Cyber wins on 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, Mercury 2.5, GPT-5.6 Cyber or DeepSeek V4 Flash Vision Exp?
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); GPT-5.6 Cyber costs $12.50 input / $75.00 output per million tokens (official OpenAI API price). 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 $28.13 for GPT-5.6 Cyber (87× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mercury 2.5 has not been scored yet, GPT-5.6 Cyber has not been scored yet and DeepSeek V4 Flash Vision Exp has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury 2.5, GPT-5.6 Cyber and DeepSeek V4 Flash Vision Exp 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 400,000 for GPT-5.6 Cyber and 260,000 for Mercury 2.5. Maximum output per response: Mercury 2.5 up to 65,536, GPT-5.6 Cyber up to 128,000, DeepSeek V4 Flash Vision Exp up to 384,000 tokens.
Which can read images, PDFs, audio or video?
Mercury 2.5 accepts text; GPT-5.6 Cyber accepts text and images; DeepSeek V4 Flash Vision Exp accepts text and images. GPT-5.6 Cyber handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Flash Vision Exp publishes its weights (MIT) and can be self-hosted; Mercury 2.5 and GPT-5.6 Cyber is proprietary.
Which is newer?
Mercury 2.5 is the newest, released Sep 8, 2026. DeepSeek V4 Flash Vision Exp came out Aug 21, 2026; GPT-5.6 Cyber came out Aug 7, 2026. Knowledge cutoff: GPT-5.6 Cyber Feb 16, 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.
