Models · Compare
Yi-34B-Chat vs Gemini 1.5 Pro
Rows highlighted in warm gray are where the models differ. Numbers carry their as-of date and primary source.
Specs
| Field | A: Yi-34B-Chat | B: Gemini 1.5 Pro |
|---|---|---|
| Released | 2023-11-23 | 2024-02-15 |
| Developer | 01.AI | Google DeepMind |
| Openness | Open weights | Proprietary |
| License | Apache 2.0 | Proprietary |
| OSI-approved | yes | no |
| Data released | no | no |
| Training code | yes | no |
| Architecture | dense | unknown |
| Total params | 34B | — |
| Active params | — | — |
| Experts | — | — |
| Context window | 4K | 2.1M |
| Attention | gqa | unknown |
| Position enc. | rope | unknown |
| Pretraining tokens | 3.0T | — |
| Post-training | sft | rlhf |
| Training hardware | — | — |
| $/M input | — | $0.00 |
| $/M output | — | $0.00 |
| Output tok/sec | — | 0 |
Benchmarks
Missing scores render as not reported; never inferred. Bold highlights the leader per benchmark.
General reasoning
| MMLU-Pro | — | 75.0 2026-05-21 |
| GPQA-Diamond | — | 58.9 2026-05-21 |
Code
| LiveCodeBench | — | 31.6 2026-05-21 |
Math
| MATH | — | 87.6 2026-05-21 |
| AIME 2024 | — | 23.0 2026-05-21 |
Context · A
Kai-Fu Lee's 01.AI released Yi-34B and the chat variant in late 2023, positioning a Chinese-developed open-weights model at the top of English and Chinese leaderboards. The Llama-compatible architecture meant existing Llama tooling worked unmodified, and the Apache 2.0 license cleared commercial use without an MAU cap.
Context · B
Google's first long-context Gemini checkpoint, introduced with a 128K standard window and a 1M token preview tier. Google described the design as a mixture-of-experts that activates a subset of expert networks per input, and demonstrated 99% recall on needle-in-a-haystack across 1M tokens at launch. The context window was later extended to 2M tokens in private preview, announced May 14 2024.