# DiffusionGemma 26B A4B IT vs Recursive Flint

> Recursive Flint scores 61 (C) to DiffusionGemma 26B A4B IT's 50 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint
- Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.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: 2026-10-22

Recursive Flint scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 3 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk. Both do open-weight models you run yourself.

- DiffusionGemma 26B A4B IT: grade D, 50/100, rank #99 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json
- Recursive Flint: grade C, 61/100, rank #49 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3023 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### DiffusionGemma 26B A4B IT (D)

Good for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4

Ahead on:
- Risk, 70 against 60

Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band

### Recursive Flint (C)

Good for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.

Ahead on:
- Reach, 75 against 50
- Agent interface, 90 against 70
- Documentation and upkeep, 67 against 60

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5

Watch for: No independent benchmark results yet, every published score is Recursive's


## Score by category

| Category | Weight | DiffusionGemma 26B A4B IT | Recursive Flint | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 75 | Recursive Flint +25 |
| Risk | 20% (20 this run) | 70 | 60 | DiffusionGemma 26B A4B IT +10 |
| Agent interface | 10% (10 this run) | 70 | 90 | Recursive Flint +20 |
| Documentation and upkeep | 10% (10 this run) | 60 | 67 | Recursive Flint +7 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50 · D** | **61 · C** | |

## Facts side by side

| Fact | DiffusionGemma 26B A4B IT | Recursive Flint |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Recursive |
| 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 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-15 | 2026-10-21 |
| 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 | 2.5/5 (4) | 3.5/5 (4) |

## Verdicts

**DiffusionGemma 26B A4B IT.** A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.

**Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

## Before you call either

### DiffusionGemma 26B A4B IT

1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions
2. In Transformers, install `transformers>=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype="auto"`; generation_config.json already holds the recommended sampler
3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `<|think|>` at the start of the system prompt, read the reasoning between `<|channel>thought` and `<channel|>`, and drop it from history before the next turn
4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself
5. Streaming arrives one 256-token canvas at a time, not token by token
6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs

### Recursive Flint

1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on
3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
6. Message content must be a string, flatten content-part arrays first
7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key

## Questions

### Which is better for AI agents, DiffusionGemma 26B A4B IT or Recursive Flint?

Recursive Flint scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 3 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk.


## For agents

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

## Other comparisons with DiffusionGemma 26B A4B IT or Recursive Flint

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