Gemini 4 Carbon vs Mistral Large 4
Gemini 4 Carbon and Mistral Large 4 score 89.4% vs 56.8% averaged across five Vision Evals tasks (#1 and #46 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins all 5 tasks, with its widest lead on reasoning (91.4% vs 38.9%).
- Mistral Large 4 is about half the cost: $0.0033 vs $0.0061 overall average cost per sample.
- Gemini 4 Carbon is faster: 8.7s vs 31.2s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Carbon vs Mistral Large 4 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Carbon vs Mistral Large 4 Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Carbon | Mistral Large 4 |
|---|---|---|
| Organization | Mistral | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Oct 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 1.05T total, 49B active |
| License | Proprietary | Custom |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.700 |
| Output $/1M | No published price | $2.10 |
| Vision Tasks | ||
| Phrase Grounding | Not listed | 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% | 56.8% |
| Avg cost / sample | $0.0061 | $0.0033 |
| Avg speed / sample | 8.71s | 31.23s |
| By task | ||
| Object Detection (low) | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 | 59.3% ±0.7, Mean of 3 runs, range 58.5 to 60.0 |
| Object Detection (high) | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 | 50.2% ±2.5, Mean of 3 runs, range 48.0 to 53.0 |
| Counting (low) | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 54.5% ±0.7, Mean of 3 runs, range 54.0 to 55.4 |
| Counting (high) | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 | 63.1% ±2.0, Mean of 3 runs, range 60.8 to 64.9 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 85.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 87.2% | 46.1% |
| by category |
|
|
| OCR (high) | 89.7% | 54.5% |
| by category |
|
|
| Reasoning (low) | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 | 38.9% ±0.3, Mean of 3 runs, range 38.4 to 39.1 |
| Reasoning (high) | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 | 57.6% ±2.0, Mean of 3 runs, range 55.6 to 59.6 |
Gemini 4 Carbon vs Mistral Large 4: 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.
Mistral Large 4, nicknamed Le Chonk, is a natively multimodal mixture-of-experts model from Mistral that accepts interleaved text and image input and produces text output. It uses a granular MoE design with roughly 1.05 trillion total parameters and 49 billion active per token, reported as 52 billion when embeddings and output layers are counted, paired with a 1.6 billion parameter vision encoder and a context window of one million tokens. The model is trained from scratch on about 3,800 NVIDIA Grace Blackwell GPUs in Mistral's European data centers and supports more than 160 languages. It behaves as a hybrid instruct and reasoning system, with a reasoning effort setting that selects between direct answers and longer deliberation, alongside function calling and structured output for agentic workflows.
Image understanding is a focus of this generation, covering documents, charts, technical drawings and natural scenes, and the model emits bounding box coordinates for visual grounding queries. Reported grounding results include 42 percent on Dense200 and 73 percent on the DIOR-RSVG remote sensing benchmark. Mistral describes agentic vision workflows in which the model zooms into gigapixel satellite imagery or engineering drawings to verify details, and reports coding results such as 62 percent on DeepSWE. Figures published at preview time are preliminary because the reinforcement learning phase is still in progress.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 4 Carbon performed better. It scores higher on all five vision tasks and averages 89.4% (#1 of 68) against 56.8% (#46 of 68) for Mistral Large 4. 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 38.9%. This is the widest gap between the two models across the benchmark's tasks.
Mistral Large 4 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 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 31.2s. 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.