deepseek-v4-flash
Input price$0.15
Output price$0.6
InceptionText
mercury-2.5-preview
mercury-2.5-preview is Inception's model at $0.04 input and $0.15 output per million tokens, with a 262K context window. On a typical 3:1 input-to-output mix that blends to roughly $0.068 per million tokens, placing it in the budget band of the 79 models tracked here — cheaper than 97% of them.
Context window262K
Input price$0.04 / 1M
Output price$0.15 / 1M
Output speed1107 tok/s
Quality score82
Data updatedUpdated Sep 2026
Price verified2026-09-16 · OpenRouter
Price noteLaunch promotion, 80% off the $0.20 / $0.75 standard rate. The preview tier and the GA mercury-2.5 are two tiers of the same model
By price, mercury-2.5-preview ranks 3rd cheapest of 79 on input and 6th on output. Its quality score of 82 ranks 56th of 79, and on value (quality score ÷ output price) it ranks 5th of 79. Its blended rate sits 98% below the catalog average.
In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $7.75 per month on mercury-2.5-preview, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $8.80. Because output is priced 3.8× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.
A 262K context window holds roughly 349 pages of A4 text (at ~750 tokens per page), ranking 43rd of 79 here. That covers typical RAG and multi-turn chat, though book-length inputs still need retrieval and chunking. Output runs at about 1107 tok/s (1st fastest of 79), fast enough for real-time interaction and high-throughput batches.
We track 1 Inception models, and mercury-2.5-preview's blended rate sits 0% above that provider's average. If your task tolerates a little less headroom, qwen3.7-flash (Alibaba) blends about 19% cheaper at a quality score of 82, and is worth benchmarking first. Going the other way, Hunyuan HY3 scores 87 on quality for about 48% more.
Estimated monthly cost
Monthly API spend across four common workloads, next to the cheapest model we track.
| Workload | Usage assumption | This model | Cheapest model |
|---|---|---|---|
| Support chatbot | 2,000 input / 500 output tokens per turn, 50,000 turns per month | $7.75 | $3.00 |
| RAG document Q&A | 8,000 input / 800 output tokens per query, 20,000 queries per month | $8.80 | $3.84 |
| Coding assistant | 12,000 input / 2,000 output tokens per request, 10,000 requests per month | $7.80 | $3.20 |
| Batch summarization | 4,000 input / 400 output tokens per item, 200,000 items per month | $44 | $19 |
Derived directly from the rates on this page. Excludes prompt caching, batch discounts and free tiers.
How it compares
The alternatives worth benchmarking alongside it, from the same dataset.
| Model | Provider | Blended /1M | Quality | Price delta |
|---|---|---|---|---|
| mercury-2.5-preview | Inception | $0.068 | 82 | — |
| qwen3.7-flashCheaper option | Alibaba | $0.055 | 82 | -19% |
| Hunyuan HY3Higher quality | Tencent | $0.10 | 87 | +48% |
Best for
FastAffordableChatbotCodingLong Context
Modalities
Text
Where it fits — and where it doesn't
Good fit
- Coding: writing, refactoring and debugging across multi-file project context.
- Chat and support: natural multi-turn conversation for user-facing products.
- High throughput and real-time UX: 1107 tok/s keeps streamed responses feeling instant.
- High-volume, cost-sensitive batch work: low rates suit classification, tagging and summarization at scale.
Poor fit
- Image understanding: this model is text-only, so vision tasks need a multimodal alternative.
Frequently asked questions
How much does the mercury-2.5-preview API cost?
mercury-2.5-preview costs $0.04 per million input tokens and $0.15 per million output tokens. On a 3:1 input-to-output mix that blends to about $0.068 per million tokens.
What does mercury-2.5-preview cost per month?
It depends on volume. Using the four worked scenarios on this page: a support chatbot runs about $7.75/month, RAG document Q&A about $8.80/month, a coding assistant about $7.80/month, and batch summarization about $44/month. Plug your own token counts into the cost calculator for a tailored figure.
How large is the mercury-2.5-preview context window?
262K tokens — roughly 349 pages of A4 text at ~750 tokens per page, ranking 43rd of the 79 models tracked here.
mercury-2.5-preview or qwen3.7-flash — which is better value?
qwen3.7-flash blends to about $0.055 per million tokens versus $0.068 for mercury-2.5-preview — roughly 19% cheaper — at a quality score of 82 against 82. If that quality gap does not show up on your task, take the cheaper one; if it does, stay with mercury-2.5-preview. Benchmark both on your own data before deciding.
What is mercury-2.5-preview best suited for?
Tagged use cases: fast, cheap, chatbot, coding, long-context. Coding: writing, refactoring and debugging across multi-file project context. Less suitable: Image understanding: this model is text-only, so vision tasks need a multimodal alternative.
How current is this mercury-2.5-preview pricing?
Pricing here was last verified on 2026-09-16 against OpenRouter. llmprice.app runs a daily collection job, but providers can change rates between runs — confirm against Inception's official pricing page before you commit.
Estimate the real cost of this model
Bring tokens per request and monthly volume into the calculator to compare total model costs.
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Pricing, speed and quality scores change over time. Confirm official documentation before production use.