# Olmo 3 7B Think vs Qwen3.5-122B-A10B

> Qwen3.5-122B-A10B scores 63.6 (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/olmo-3-7b-think-vs-qwen3-5-122b-a10b
- Markdown: https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.md (~6,350 tokens)
- Slim: https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.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

Qwen3.5-122B-A10B scores 63.6 (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 risk and documentation and upkeep. Both do open-weight models you run yourself.

- 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
- Qwen3.5-122B-A10B: grade B, 63.6/100, rank #22 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.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

### 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:
- Risk, 75 against 55
- Documentation and upkeep, 52 against 39

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

### Qwen3.5-122B-A10B (B)

Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.

Ahead on:
- Capability, 62 against 28
- Reach, 80 against 75
- Agent interface, 70 against 35

Watch for: Too big for a single 64 GB machine even at 4 bits


## Score by category

| Category | Weight | Olmo 3 7B Think | Qwen3.5-122B-A10B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 28 | 62 | Qwen3.5-122B-A10B +34 |
| Reach | 25% (25 this run) | 75 | 80 | Qwen3.5-122B-A10B +5 |
| Risk | 20% (20 this run) | 75 | 55 | Olmo 3 7B Think +20 |
| Agent interface | 10% (10 this run) | 35 | 70 | Qwen3.5-122B-A10B +35 |
| Documentation and upkeep | 10% (10 this run) | 52 | 39 | Olmo 3 7B Think +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.3 · D** | **63.6 · B** | |

## Facts side by side

| Fact | Olmo 3 7B Think | Qwen3.5-122B-A10B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Ai2 (Allen Institute for AI) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-06-25 | 2026-02-24 |
| 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) | 3/5 (4) |

## Verdicts

**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.

**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.

## Before you call either

### 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

### Qwen3.5-122B-A10B

1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)
2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do
4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text
8. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, Olmo 3 7B Think or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B scores 63.6 (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 risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "olmo-3-7b-think", "b": "qwen3-5-122b-a10b"}`. From a terminal: `anchor compare olmo-3-7b-think qwen3-5-122b-a10b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json

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- [Olmo 3 7B Think vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b.md)
- [Olmo 3 7B Think vs Qwen3.5-2B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b.md)
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- [Olmo 3 7B Think vs Qwen3.5-4B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b.md)
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- [Olmo 3 7B Think vs Qwen3.6-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b.md)
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- [Olmo 3 7B Think vs Qwen3.8-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b.md)
- [Olmo 3 7B Think vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.md)
- [Olmo 3 7B Think vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct.md)
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- [Olmo 3 7B Think vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.md)
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- [Olmo 3 7B Think vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.md)
- [Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.md)
- [Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.md)
- [Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.md)
- [Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.md)
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- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-122b-a10b.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b.md)
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- [Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b.md)
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- [Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next.md)
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- [Qwen3.5-122B-A10B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next.md)
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- [Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.md)
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- [Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.5-122B-A10B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.md)
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- [Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.md)

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