ModelRefs / BGE-M3 vs Gemini 3 Pro
BGE-M3 vs Gemini 3 Pro
BGE-M3 vs Gemini 3 Pro: side-by-side benchmarks, pricing, context windows, coding ability and deployment. Pick the right model for your stack.
Overview
BGE-M3: BAAI's 568M-parameter multilingual embedding and retrieval model supporting dense, sparse, and multi-vector representations with inputs up to 8,192 tokens.
Gemini 3 Pro: Gemini 3 Pro is Google DeepMind's flagship multimodal model with a 2M-token context window spanning text, image, audio, and video inputs. It is built for long-context reasoning, document understanding, and multimodal agentic workflows.
Context window: BGE-M3 accepts up to 8,192 tokens, Gemini 3 Pro up to 2,000,000. Confirm the limit for your specific deployment channel before relying on it.
BGE-M3 vs Gemini 3 Pro at a glance
| Attribute | BGE-M3 | Gemini 3 Pro |
|---|---|---|
| Provider | BAAI | |
| Released | 2024-01-30 | 2025-11-18 |
| Context window | 8,192 tokens | 2,000,000 tokens |
| Input price | Free / self-hosted | $1.25/M tokens |
| Output price | Free / self-hosted | $10/M tokens |
| Licence | MIT | Proprietary |
| Self-hostable | Yes, open weights | No, hosted only |
| Modalities | text, Embedding, Open Source, Multilingual, BAAI | text, vision, audio, video |
Where they differ most
- Cost Efficiency: BGE-M3 100%, Gemini 3 Pro 47%. BGE-M3 leads on this dimension.
Capability scores are ModelRefs' own derived signals, not vendor claims or benchmark results. Validate against your own workload before relying on them.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to BGE-M3 vs Gemini 3 Pro.
Frequently asked questions
Which is better, BGE-M3 or Gemini 3 Pro?
BGE-M3 and Gemini 3 Pro target different workloads — see the benchmark and pricing tables for a side-by-side answer.
Is BGE-M3 cheaper than Gemini 3 Pro?
Compare Free — open weights vs $0.00125 on this page.