# Cosmos-Reason2-2B vs Gemma 4 26B A4B IT

> Gemma 4 26B A4B IT scores 72.2 (BB) to Cosmos-Reason2-2B's 59.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it
- Markdown: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.md (~6,450 tokens)
- Slim: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.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

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Cosmos-Reason2-2B: grade C, 59.5/100, rank #59 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/cosmos-reason2-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json
- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.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

### Cosmos-Reason2-2B (C)

Good for: Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces

Watch for: Gated behind a click-through licence that needs a Hugging Face account

### Gemma 4 26B A4B IT (BB)

Good for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context

Ahead on:
- Reach, 90 against 65
- Risk, 70 against 60
- Agent interface, 85 against 65
- Documentation and upkeep, 65 against 39

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.5 out of 5

Watch for: No safety evaluation figures published


## Score by category

| Category | Weight | Cosmos-Reason2-2B | Gemma 4 26B A4B IT | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 90 | Gemma 4 26B A4B IT +25 |
| Risk | 20% (20 this run) | 60 | 70 | Gemma 4 26B A4B IT +10 |
| Agent interface | 10% (10 this run) | 65 | 85 | Gemma 4 26B A4B IT +20 |
| Documentation and upkeep | 10% (10 this run) | 39 | 65 | Gemma 4 26B A4B IT +26 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.5 · C** | **72.2 · BB** | |

## Facts side by side

| Fact | Cosmos-Reason2-2B | Gemma 4 26B A4B IT |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Google DeepMind |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-10 | 2026-07-20 |
| Terms last updated |  | no document linked |
| Privacy policy last updated | no document linked | 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 | 2.5/5 (4) | 3.8/5 (4) |

## Verdicts

**Cosmos-Reason2-2B.** A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.

**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.

## Before you call either

### Cosmos-Reason2-2B

1. Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading
2. Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{"video": {"num_frames": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises
3. For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path "$(pwd)/media"`, so the server can read only that folder, not everything under the directory you start it from
4. Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off
5. For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply
6. Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself
7. Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks

### Gemma 4 26B A4B IT

1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images
2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K
3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text
4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
5. Validate tool names and arguments before running them, and check any JSON yourself

## Questions

### Which is better for AI agents, Cosmos-Reason2-2B or Gemma 4 26B A4B IT?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Cosmos-Reason2-2B's 59.5 (C), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cosmos-reason2-2b", "b": "gemma-4-26b-a4b-it"}`. From a terminal: `anchor compare cosmos-reason2-2b gemma-4-26b-a4b-it`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json

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