MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

Qwen3.8-Max vs Gemini 3.5 Flash

Qwen3.8-Max wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorAlibaba / Google
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Qwen3.8-Max when coding depth, long-context reliability matter most; choose Gemini 3.5 Flash when lower cost, lower latency is the priority.

Choose Qwen3.8-Max if you…

  • Top-tier Chinese
  • Balanced multimodal
  • Alibaba Cloud ecosystem
  • Good value
  • Best for: Chinese tasks, Multimodal, Coding

Choose Gemini 3.5 Flash if you…

  • Extremely fast
  • Low price
  • Native multimodal
  • Built for scale
  • Best for: Fast tasks, Multimodal, Low cost
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

Qwen3.8-Max Higher overall
Alibaba · #4 overall
Overall82
Coding77
Multimodal92
VS
Gemini 3.5 Flash
Google · #14 overall
Overall69
Coding64
Multimodal86

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionQwen3.8-MaxGemini 3.5 FlashVerdict
VendorAlibaba (CN)Google (US)Different vendors
Released2026.072026.07Gemini 3.5 Flash is newer
Overall (rank)82 · #469 · #14Qwen3.8-Max +13
Coding77 · #664 · #17Qwen3.8-Max +13
Multimodal92 · #286 · #7Qwen3.8-Max +6
Context window1M1MTie
Max output128K128KTie
Effective-context9588Qwen3.8-Max more reliable
Input $/1M$2.5$1.5Gemini 3.5 Flash cheaper
Output $/1M$7.5$9Qwen3.8-Max cheaper
Cache discount80% offnoneQwen3.8-Max deeper
Speed~55 tok/s~80 tok/sGemini 3.5 Flash faster
TTFT0.6s0.3sGemini 3.5 Flash snappier
Function calling8875Qwen3.8-Max ahead
Refusal rate~10%~10%Tie
English7885Gemini 3.5 Flash
Chinese9872Qwen3.8-Max
Modalitiestext, imagetext, image, audio, videodifferent coverage
Open weightsNoNoBoth closed
Fine-tuningYesYes
Free tierFree credits for new Alibaba Cloud Bailiang usersGemini App free; API free tier
SOC2 / no-trainno / yesyes / yes
Private deploymentYesNoQwen3.8-Max

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Qwen3.8-Max leads the overall aggregate by 13 points (82 vs 69). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Gemini 3.5 Flash remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a clear gap: Qwen3.8-Max scores 77 against 64. On multi-file edits, SWE-style tickets and long-horizon agent loops Qwen3.8-Max needs fewer correction turns; Gemini 3.5 Flash is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Qwen3.8-Max leads multimodal 92 vs 86. A concrete modality difference: Gemini 3.5 Flash additionally handles audio, video.

Speed & latency

Gemini 3.5 Flash is faster in interactive use: ~80 tok/s with 0.3s TTFT versus ~55 tok/s with 0.6s TTFT (about 1.5× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 1M for Qwen3.8-Max and 1M for Gemini 3.5 Flash. Effective-context scores point the same way as window size — Qwen3.8-Max is ahead on usable recall (95 vs 88), so prefer it for long-document work where details cannot be missed.

Price & total cost

Gemini 3.5 Flash is the cheaper API at $1.5/$9 versus Qwen3.8-Max at $2.5/$7.5 per 1M input/output tokens — list input is about 1.7× lower. Cache discounts (Qwen3.8-Max 80%) shift the effective bill, worked out below.

Chinese vs English

English: Qwen3.8-Max 78 vs Gemini 3.5 Flash 85. Chinese: 98 vs 72. For Chinese-language production, Qwen3.8-Max is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Qwen3.8-Max 88 vs Gemini 3.5 Flash 75, so Qwen3.8-Max has the edge on structured tool use. Fine-tuning is available from Qwen3.8-Max and Gemini 3.5 Flash. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthQwen3.8-MaxGemini 3.5 FlashGap
List priceno cache applied$475100M in × $2.5  +  30M out × $7.5$420100M in × $1.5  +  30M out × $91.13×gap
With caching90% of inputs cache-hit$29590M cached in × $0.5  +  10M in × $2.5  +  30M out × $7.5$420no published cache discount1.42×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Qwen3.8-Max (coding 77 vs 64).
IF you serve real-time users and latency is a product KPI  →  choose Gemini 3.5 Flash (~80 tok/s, 0.3s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Gemini 3.5 Flash ($$1.5/$$9 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Qwen3.8-Max (effective context 95 vs 88).
07

Frequently asked questions

Which is better for agentic coding?
Qwen3.8-Max is decisively stronger for coding (77 vs 64 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; Gemini 3.5 Flash is fine for routine scripts.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Qwen3.8-Max is about $295/month and Gemini 3.5 Flash about $420/month after cache discounts ($475 and $420 at list).
Does the bigger context window actually matter?
Nominal windows are Qwen3.8-Max (1M) and Gemini 3.5 Flash (1M), but usable recall follows the effective-context score (95 vs 88). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
Qwen3.8-Max is the Chinese model (Chinese score 98, domestic cloud, possible private deployment) while Gemini 3.5 Flash is the global model (Chinese 72, overseas API). Pick by language quality, access path and where data must reside.
Which handles multimodal inputs better?
Multimodal scores are 92 (Qwen3.8-Max) vs 86 (Gemini 3.5 Flash), with modality coverage text/image versus text/image/audio/video. Match the model to the input types your product actually receives.
What is the single-line recommendation?
Choose Qwen3.8-Max for Chinese tasks, Multimodal; choose Gemini 3.5 Flash for Fast tasks, Multimodal.