# Hy3 vs Olmo 3 7B Think

> Hy3 scores 62.6 (B) to Olmo 3 7B Think's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think
- Markdown: https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.md (~5,800 tokens)
- Slim: https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Hy3 scores 62.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk. Both do open-weight models you run yourself.

- Hy3: grade B, 62.6/100, rank #27 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.json
- Olmo 3 7B Think: grade D, 52.3/100, rank #86 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Hy3 (B)

Good for: Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0

Ahead on:
- Capability, 57 against 28
- Agent interface, 75 against 35

Watch for: 597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build

### Olmo 3 7B Think (D)

Good for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand

Ahead on:
- Reach, 75 against 70
- Risk, 75 against 60

Watch for: Not trained for tool calling, and the tool format lives only in the template


## Score by category

| Category | Weight | Hy3 | Olmo 3 7B Think | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 57 | 28 | Hy3 +29 |
| Reach | 25% (25 this run) | 70 | 75 | Olmo 3 7B Think +5 |
| Risk | 20% (20 this run) | 60 | 75 | Olmo 3 7B Think +15 |
| Agent interface | 10% (10 this run) | 75 | 35 | Hy3 +40 |
| Documentation and upkeep | 10% (10 this run) | 56 | 52 | Hy3 +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.6 · B** | **52.3 · D** | |

## Facts side by side

| Fact | Hy3 | Olmo 3 7B Think |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (Hy team) | Ai2 (Allen Institute for AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-20 | 2026-06-25 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | no document linked |  |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.8/5 (4) | 2.8/5 (4) |

## Verdicts

**Hy3.** Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.

**Olmo 3 7B Think.** A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

## Before you call either

### Hy3

1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3
2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows
3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error
4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content
5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe
6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling
7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers
8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers

### Olmo 3 7B Think

1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB
2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend
3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research

## Questions

### Which is better for AI agents, Hy3 or Olmo 3 7B Think?

Hy3 scores 62.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy3", "b": "olmo-3-7b-think"}`. From a terminal: `anchor compare hy3 olmo-3-7b-think`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json

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