Head-to-head AI model comparison graphic: Gemini 3.8 Flash (Google) vs Qwen3.8 Max (Alibaba). Intelligence index 30.8 vs 33.7; output speed 121 vs 38 tokens per second; time to first token 27s vs 3.2s; output price $3.75 vs $6.00 per million tokens; input price $0.75 vs $2.00 per million tokens. franklineh.com
Gemini 3.8 Flash vs Qwen3.8 Max: head-to-head AI benchmark comparison. Gemini 3.8 Flash leads on output speed, output price and input price; Qwen3.8 Max leads on intelligence index and time to first token.
Head to headUpdated

Gemini 3.8 Flash High vs Qwen3.8 Max 0902 compared

Qwen3.8 Max 0902 leads the intelligence index 33.7 to 30.8. Gemini 3.8 Flash High costs 50% less on blended price. See token costs, speed and which to pick.

Pick it for Gemini 3.8 Flash · High

  • Faster output once writing: 121 tokens per second against 38 tokens per second, which wins on total time beyond about 1,300 tokens.
  • Lower cost: $1.50 against $3.20 for 1 million input and 200,000 output tokens.

Pick it for Qwen3.8 Max · 0902

  • Shorter wait for the first token: 3.2 seconds against 27 seconds.
  • Higher intelligence index: 33.7 against 30.8.

By the numbers

MetricGemini 3.8 Flash (High)Qwen3.8 Max (0902)
Intelligence Index30.833.7
Input price (per 1M tokens)$0.75$2.00
Output price (per 1M tokens)$3.75$6.00
Output speed (tokens/s)12138
Time to first token27s3.2s
Release date2026-09-022026-09-02

Gemini 3.8 Flash (High) from Google and Qwen3.8 Max (0902) from Alibaba land within 2.9 points of each other on the intelligence index. Throughput is the standout difference: 121 tokens per second for Gemini 3.8 Flash High against 38 tokens per second for Qwen3.8 Max 0902. No hands-on testing went into this write-up; it summarises the recorded benchmark, price and speed data for both models.

Throughput and time to first token

Gemini 3.8 Flash High generates about 121 tokens per second and Qwen3.8 Max 0902 about 38 tokens per second. Gemini 3.8 Flash High is ahead by 217% on throughput. Time to first token is 27 seconds for Gemini 3.8 Flash High and 3.2 seconds for Qwen3.8 Max 0902. That is a genuine trade-off: Qwen3.8 Max 0902 starts sooner, while Gemini 3.8 Flash High finishes first once outputs run beyond about 1,300 tokens.

Add the two numbers up for a 1,500-token reply and Gemini 3.8 Flash High finishes first: 40 seconds for Gemini 3.8 Flash High, 42 seconds for Qwen3.8 Max 0902.

How the intelligence index compares

Qwen3.8 Max 0902 comes out ahead on the intelligence index, 33.7 to 30.8. The lead is visible but narrow, so it should not decide the choice on its own. Put another way, Gemini 3.8 Flash High would need 9% more on this index to draw level.

Among 109 active scored models, Gemini 3.8 Flash High sits 50th and Qwen3.8 Max 0902 44th.

Specialist coding and math indexes are missing for both, so treat the overall score as the only capability signal here.

Token pricing and a sample bill

The list prices are $0.75 in and $3.75 out for Gemini 3.8 Flash High, and $2.00 in and $6.00 out for Qwen3.8 Max 0902, per million tokens. For output tokens, Qwen3.8 Max 0902 is 1.6 times the price of Gemini 3.8 Flash High.

For a sample workload of 1 million input tokens and 200,000 output tokens, Gemini 3.8 Flash High would cost $1.50 and Qwen3.8 Max 0902 $3.20, a difference of $1.70. A bigger example, summarising 1,000 documents of 8,000 tokens into 400-token summaries, costs $7.50 on Gemini 3.8 Flash High and $18.40 on Qwen3.8 Max 0902: a gap of $10.90.

Cost per point of intelligence

Per point of intelligence index, Gemini 3.8 Flash High costs $0.049 of blended price per million tokens and Qwen3.8 Max 0902 $0.089. Qwen3.8 Max 0902 costs 1.8 times as much per point, which is the price of its lead.

When they came out

Gemini 3.8 Flash High and Qwen3.8 Max 0902 share a release date: September 2, 2026.

Common questions about Gemini 3.8 Flash High and Qwen3.8 Max 0902

Is Gemini 3.8 Flash High better than Qwen3.8 Max 0902?

Not across the board: Qwen3.8 Max 0902 scores higher on the intelligence index, 33.7 to 30.8; Gemini 3.8 Flash High is 50% cheaper on blended price; Gemini 3.8 Flash High streams output 3.2 times as fast; and Qwen3.8 Max 0902 reaches its first token sooner.

Where do these numbers come from?

The figures are the recorded benchmark, price and speed data in Franklin AI's model catalogue. This page is written from that data automatically; neither model was tested by hand for it.

Which is faster, Gemini 3.8 Flash High or Qwen3.8 Max 0902?

Gemini 3.8 Flash High generates output faster, at 121 tokens per second against 38 tokens per second. Qwen3.8 Max 0902 reaches its first token sooner (3.2 seconds against 27 seconds). Counting the first-token wait, Qwen3.8 Max 0902 finishes short replies first and Gemini 3.8 Flash High pulls ahead beyond about 1,300 tokens.

Who makes Gemini 3.8 Flash High and Qwen3.8 Max 0902?

Gemini 3.8 Flash High is made by Google and Qwen3.8 Max 0902 by Alibaba. Gemini 3.8 Flash High was released on September 2, 2026 and Qwen3.8 Max 0902 on September 2, 2026.

Which is cheaper, Gemini 3.8 Flash High or Qwen3.8 Max 0902?

Gemini 3.8 Flash High is cheaper on a blended basis. Gemini 3.8 Flash High lists $0.75 per million input tokens and $3.75 per million output tokens; Qwen3.8 Max 0902 lists $2.00 and $6.00. For 1 million input and 200,000 output tokens that is $1.50 against $3.20.

Final whistle

This one is genuinely split between Gemini 3.8 Flash High and Qwen3.8 Max 0902, and the right answer depends on which trade-off you can live with. Pick Gemini 3.8 Flash High if your outputs often run past about 1,300 tokens and cost matters ($3.75 against $6.00 per million output tokens). Pick Qwen3.8 Max 0902 if users are waiting on the first word and you need the higher overall score (33.7 against 30.8).

Where the numbers come from

The scores, speed and prices in this comparison come from independent third-party model evaluations that Franklin AI syncs each day, and the article was written from a snapshot of them. For the latest results, open the Gemini 3.8 Flash (High) and Qwen3.8 Max (0902) pages. The AI Benchmark methodology explains how that data is collected and shown.

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