# Gemma 4 26B A4B IT vs Nanbeige4.3-33B

> Gemma 4 26B A4B IT scores 72.2 (BB) to Nanbeige4.3-33B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.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: 2026-10-25

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 111 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
- Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### 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 55
- Risk, 70 against 60
- Documentation and upkeep, 65 against 56

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: No safety evaluation figures published

### Nanbeige4.3-33B (C)

Good for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.

Watch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run


## Score by category

| Category | Weight | Gemma 4 26B A4B IT | Nanbeige4.3-33B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 55 | Gemma 4 26B A4B IT +35 |
| Risk | 20% (20 this run) | 70 | 60 | Gemma 4 26B A4B IT +10 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 65 | 56 | Gemma 4 26B A4B IT +9 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.2 · BB** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 26B A4B IT | Nanbeige4.3-33B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Nanbeige LLM Lab (BOSS Zhipin) |
| 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 (Hub metadata only; the repository has no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-20 | 2026-10-24 |
| Terms last updated | no document linked | 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 | 3.8/5 (4) | none |

## Verdicts

**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.

**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.

## Before you call either

### 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

### Nanbeige4.3-33B

1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers
2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes
3. Pass `chat_template_kwargs` `{"preserve_thinking": true}` for multi-turn tool use and coding, and `{"enable_thinking": false}` to skip reasoning, as the card recommends
4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card
5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed
6. Send one system message at the start only, as the template raises an error for a later one
7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed

## Questions

### Which is better for AI agents, Gemma 4 26B A4B IT or Nanbeige4.3-33B?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-26b-a4b-it", "b": "nanbeige-4-3-33b"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it nanbeige-4-3-33b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json

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