Gemini 3.8 Flash vs Qwen3.8 Max
Gemini 3.8 Flash and Qwen3.8 Max score 79.8% vs 79.3% averaged across five Vision Evals tasks (#8 and #9 of 65 models), so they are effectively tied.
- Qwen3.8 Max wins 3 of the 5 tasks, with its widest lead on object detection (76.7% vs 68.1%).
- Gemini 3.8 Flash is about half the cost: $0.0045 vs $0.0080 overall average cost per sample.
- Gemini 3.8 Flash is faster: 12.8s vs 18.9s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 3.8 Flash vs Qwen3.8 Max on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 3.8 Flash vs Qwen3.8 Max Comparison Table
Evals updated October 26, 2026Pricing updated November 3, 2026
| Property | Gemini 3.8 Flash | Qwen3.8 Max |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 1.0M | 984K |
| Parameters | Unknown | 2.4T total, ~95B active |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.825 | No published price |
| Output $/1M | $4.13 | No published price |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 79.8% | 79.3% |
| Avg cost / sample | $0.0045 | $0.0080 |
| Avg speed / sample | 12.76s | 18.95s |
| By task | ||
| Object Detection (low) | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 72.8% | 74.0% |
| by category |
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| OCR (high) | 78.7% | 71.8% |
| by category |
|
|
| Reasoning (low) | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
Gemini 3.8 Flash vs Qwen3.8 Max: Overview
Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.