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

Gemini 3.1 Pro vs Qwen3.8-Max

Gemini 3.1 Pro wins on Overall; Qwen3.8-Max wins on Coding. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorGoogle / Alibaba
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Gemini 3.1 Pro when its stronger dimensions matter most; choose Qwen3.8-Max when its stronger dimensions is the priority.

Choose Gemini 3.1 Pro if you…

  • Native audio and video
  • Strong scientific computing
  • Largest 2M context
  • Search integration
  • Best for: Multimodal, Scientific reasoning, Audio & video

Choose Qwen3.8-Max if you…

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

Gemini 3.1 Pro Higher overall
Google · #3 overall
Overall83
Coding74
Multimodal92
VS
Qwen3.8-Max
Alibaba · #4 overall
Overall82
Coding77
Multimodal92

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.

DimensionGemini 3.1 ProQwen3.8-MaxVerdict
VendorGoogle (US)Alibaba (CN)Different vendors
Released2026.052026.07Qwen3.8-Max is newer
Overall (rank)83 · #382 · #4Gemini 3.1 Pro +1
Coding74 · #977 · #6Qwen3.8-Max +3
Multimodal92 · #292 · #2Tie
Context window2M1MGemini 3.1 Pro larger
Max output128K128KTie
Effective-context9295Qwen3.8-Max more reliable
Input $/1M$2$2.5Gemini 3.1 Pro cheaper
Output $/1M$12$7.5Qwen3.8-Max cheaper
Cache discountnone80% offQwen3.8-Max deeper
Speed~60 tok/s~55 tok/sGemini 3.1 Pro faster
TTFT0.7s0.6sQwen3.8-Max snappier
Function calling7888Qwen3.8-Max ahead
Refusal rate~12%~10%Qwen3.8-Max less restrictive
English9078Gemini 3.1 Pro
Chinese7598Qwen3.8-Max
Modalitiestext, image, audio, videotext, imagedifferent coverage
Open weightsNoNoBoth closed
Fine-tuningYesYes
Free tierGemini App free with limited quota; API free tier availableFree credits for new Alibaba Cloud Bailiang users
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesQwen3.8-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

Gemini 3.1 Pro leads the overall aggregate by 1 points (83 vs 82). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Qwen3.8-Max remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a modest gap: Qwen3.8-Max scores 77 against 74. On multi-file edits, SWE-style tickets and long-horizon agent loops Qwen3.8-Max needs fewer correction turns; Gemini 3.1 Pro is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

They tie on the multimodal aggregate at 92. A concrete modality difference: Gemini 3.1 Pro additionally handles audio, video.

Speed & latency

Gemini 3.1 Pro is faster in interactive use: ~60 tok/s with 0.7s TTFT versus ~55 tok/s with 0.6s TTFT (about 1.1× 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 2M for Gemini 3.1 Pro and 1M for Qwen3.8-Max. Crucially, the larger nominal window does not win on usable recall: Gemini 3.1 Pro advertises 2M but Qwen3.8-Max scores higher on effective-context (95 vs 92), i.e. it actually retains more of what it was given.

Price & total cost

Gemini 3.1 Pro is the cheaper API at $2/$12 versus Qwen3.8-Max at $2.5/$7.5 per 1M input/output tokens — list input is about 1.2× lower. Cache discounts (Qwen3.8-Max 80%) shift the effective bill, worked out below.

Chinese vs English

English: Gemini 3.1 Pro 90 vs Qwen3.8-Max 78. Chinese: 75 vs 98. 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: Gemini 3.1 Pro 78 vs Qwen3.8-Max 88, so Qwen3.8-Max has the edge on structured tool use. Fine-tuning is available from Gemini 3.1 Pro and Qwen3.8-Max. 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 monthGemini 3.1 ProQwen3.8-MaxGap
List priceno cache applied$560100M in × $2  +  30M out × $12$475100M in × $2.5  +  30M out × $7.51.18×gap
With caching90% of inputs cache-hit$560no published cache discount$29590M cached in × $0.5  +  10M in × $2.5  +  30M out × $7.51.90×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 74).
IF you serve real-time users and latency is a product KPI  →  choose Gemini 3.1 Pro (~60 tok/s, 0.7s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Qwen3.8-Max ($$2.5/$$7.5 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Qwen3.8-Max (effective context 95 vs 92).
07

Frequently asked questions

How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Gemini 3.1 Pro is about $560/month and Qwen3.8-Max about $295/month after cache discounts ($560 and $475 at list).
Does the bigger context window actually matter?
Nominal windows are Gemini 3.1 Pro (2M) and Qwen3.8-Max (1M), but usable recall follows the effective-context score (92 vs 95). 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 3.1 Pro is the global model (Chinese 75, overseas API). Pick by language quality, access path and where data must reside.
Which handles multimodal inputs better?
Multimodal scores are 92 (Gemini 3.1 Pro) vs 92 (Qwen3.8-Max), with modality coverage text/image/audio/video versus text/image. Match the model to the input types your product actually receives.
What is the single-line recommendation?
Choose Gemini 3.1 Pro for Multimodal, Scientific reasoning; choose Qwen3.8-Max for Chinese tasks, Multimodal.
How quickly do these rankings change?
Modelspectra refreshes the aggregate as new public benchmarks and prices appear. Treat scores within 3 points as a tie and re-check before a committed purchase.