Gemini 4 Carbon vs Qwen3.5 35B A3B
Gemini 4 Carbon averages 89.4% across five Vision Evals tasks and Qwen3.5 35B A3B 62.5% across four (#1 and #37 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins all 4 tasks both ran, with its widest lead on reasoning (91.4% vs 54.1%).
- Qwen3.5 35B A3B is about a quarter of the cost: $0.0016 vs $0.0061 overall average cost per sample.
- Gemini 4 Carbon is faster: 8.7s vs 31.9s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Carbon vs Qwen3.5 35B A3B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Carbon vs Qwen3.5 35B A3B Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Carbon | Qwen3.5 35B A3B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 35B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.140 |
| Output $/1M | No published price | $1.00 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 89.4% | 62.5%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0061 | $0.0016 |
| Avg speed / sample | 8.71s | 31.88s |
| By task | ||
| Object Detection (low) | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 | 52.9% ±3.2, Mean of 3 runs, range 49.5 to 55.9 |
| Object Detection (high) | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 | – |
| Counting (low) | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 62.6% ±2.0, Mean of 3 runs, range 60.8 to 64.9 |
| Counting (high) | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | – |
| OCR (low) | 87.2% | – |
| by category |
| |
| OCR (high) | 89.7% | – |
| by category |
| |
| Reasoning (low) | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 | 54.1% ±0.3, Mean of 3 runs, range 53.6 to 54.3 |
| Reasoning (high) | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 | – |
Gemini 4 Carbon vs Qwen3.5 35B A3B: Overview
Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.
The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.
Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Carbon performed better. It scores higher on all 4 vision tasks and averages 89.4% (#1 of 68) against 62.5% (#37 of 68) for Qwen3.5 35B A3B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 4 Carbon leads with 91.4% against 54.1%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 35B A3B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0016 per sample against $0.0061. Actual costs depend on your image sizes, prompts, and output length.
Gemini 4 Carbon is faster. Across Roboflow's Vision Evals it averaged 8.7s per inference against 31.9s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.