API cost decision in 10 seconds

NewMercury 2.5 Preview vs Gemini 3.5 Flash Lite (batch)

Pick Mercury 2.5 Preview for lower cost; pick Gemini 3.5 Flash Lite (batch) only if the larger context window matters more.

Page updated: 2026-09-01T15:07:22Z Data confirmed: 2026-09-01T17:07:29Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Mercury 2.5 Preview for lower cost; pick Gemini 3.5 Flash Lite (batch) only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Mercury 2.5 Preview is estimated at $0.11 vs $0.78 for Gemini 3.5 Flash Lite (batch), saving $0.66 (85.2% lower).

Cost-first pickMercury 2.5 Preview

Context-first pickGemini 3.5 Flash Lite (batch)

Sample savings$0.6685.2%

10x traffic gap$6.6

Gemini 3.5 Flash Lite (batch) has more context, but Mercury 2.5 Preview saves $0.66 on the standard workload. At 10x that traffic, the same price gap is about $6.6. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Mercury 2.5 Preview stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Mercury 2.5 Preview	Gemini 3.5 Flash Lite (batch)
Input-heavy / RAG	5M input + 500K output	Mercury 2.5 Preview	$0.28	$1.38
Balanced workload	1M input + 1M output	Mercury 2.5 Preview	$0.19	$1.4
Output-heavy chatbot	1M input + 5M output	Mercury 2.5 Preview	$0.79	$6.4

Cheaper input Mercury 2.5 Preview $0.04 vs $0.15 / 1M

Mercury 2.5 Preview is $0.11 cheaper per 1M input tokens (73.3% lower; 3.75x difference).

Cheaper output Mercury 2.5 Preview $0.15 vs $1.25 / 1M

Mercury 2.5 Preview is $1.1 cheaper per 1M output tokens (88% lower; 8.33x difference).

Larger context Gemini 3.5 Flash Lite (batch) 260K vs 1.05M

Gemini 3.5 Flash Lite (batch) has 788.58K more context (4.03x larger).

Sample workload Mercury 2.5 Preview $0.11 vs $0.78

Mercury 2.5 Preview is $0.66 cheaper on the standard workload (85.2% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Mercury 2.5 Preview Calculating… Estimated API cost

Gemini 3.5 Flash Lite (batch) Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Mercury 2.5 Preview has the lower input price; Mercury 2.5 Preview has the lower output price; Gemini 3.5 Flash Lite (batch) offers the larger context window. For the 1M input plus 500K output sample, Mercury 2.5 Preview is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.11 for Mercury 2.5 Preview and $0.78 for Gemini 3.5 Flash Lite (batch).

Best Fit

Choose Mercury 2.5 Preview when you care most about lower input-token price, and lower output-token price.

Choose Gemini 3.5 Flash Lite (batch) when you care most about larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, Mercury 2.5 Preview is estimated at $0.11 vs $0.78 for Gemini 3.5 Flash Lite (batch), saving $0.66 (85.2% lower).
Mercury 2.5 Preview is $0.66 cheaper on the standard workload (85.2% lower).
Mercury 2.5 Preview is $0.11 cheaper per 1M input tokens (73.3% lower; 3.75x difference).
Mercury 2.5 Preview is $1.1 cheaper per 1M output tokens (88% lower; 8.33x difference).
Gemini 3.5 Flash Lite (batch) has 788.58K more context (4.03x larger).

Head-to-Head Specs

Feature	NewMercury 2.5 Preview (Inception)	Gemini 3.5 Flash Lite (batch) (Google)
Input Price prompt tokens per 1M	$0.04	$0.15
Completion Price per 1M tokens	$0.15	$1.25
Sample Workload Cost 1M input + 500K output	$0.11	$0.78
Context Window	260K	1.05M
Release Date	2026-08-31	2026-07-21

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Mercury 2.5 Preview	On the standard 1M input plus 500K output workload, Mercury 2.5 Preview is estimated at $0.11 vs $0.78 for Gemini 3.5 Flash Lite (batch), saving $0.66 (85.2% lower).
High-volume input processing	Mercury 2.5 Preview	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Mercury 2.5 Preview	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Gemini 3.5 Flash Lite (batch)	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Gemma 4 26B A4B (free) can replace Gemini 3.5 Flash Lite (batch) when lower sample workload cost matters most: $0.
Gemma 4 31B (free) can replace Gemini 3.5 Flash Lite (batch) when lower sample workload cost matters most: $0.
Lyria 3 Pro Preview can replace Gemini 3.5 Flash Lite (batch) when lower sample workload cost matters most: $0.
Lyria 3 Clip Preview can replace Gemini 3.5 Flash Lite (batch) when lower sample workload cost matters most: $0.

Larger context near this budget

DeepSeek V4 Flash 0731 offers 1.31M context with $0.15 sample workload cost.
GLM 5.3 Flash offers 1.31M context with $0.2 sample workload cost.
DeepSeek V4 Flash Latest offers 1.31M context with $0.1 sample workload cost.
Llama 4 Scout offers 1.31M context with $0.25 sample workload cost.

Popular competitors

DeepSeek V4 Flash 0731 · DeepSeek · #1
GPT-5.6 Luna · OpenAI · #2
GLM 5.3 Flash · Z.ai · #3
MiMo-V2.5 · Xiaomi · #4

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Provider catalogs

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Inception catalog

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Google catalog

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Mercury 2.5 Preview

Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple tokens in parallel, achieving...

Gemini 3.5 Flash Lite (batch)

Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.