Head to head · Open-weight models you run yourself · October 2026 research run
DiffusionGemma 26B A4B IT vs Ling-3.1-flash-1019
Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and agent interface. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best 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 45
- Agent interface, 70 against 60
Watch for
No quantised build from Google, so the smallest official build needs the 64 GB band
Best for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding
Ahead on
- Reach, 60 against 55
Watch for
574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Risk DiffusionGemma 26B A4B IT +25
DiffusionGemma 26B A4B IT 70
File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma.
Ling-3.1-flash-1019 45
File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.
Agent interface DiffusionGemma 26B A4B IT +10
DiffusionGemma 26B A4B IT 70
The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20). The tool-calling format gets 10, since only the template and the shipped response schema say how tools work here and Google's tool-calling guide is for Gemma 4. Structured output scores 0, undocumented and rejected by SGLang. Thinking can be turned on or off per request with the reasoning in its own channel (10), and the context is 256K (10).
Ling-3.1-flash-1019 60
Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in <tools>) and how calls come back (<tool_call>name with <arg_key> and <arg_value> pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.
Reach Ling-3.1-flash-1019 +5
DiffusionGemma 26B A4B IT 55
Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it.
Ling-3.1-flash-1019 60
Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.
Score by category
| Category | Weight this run | DiffusionGemma 26B A4B IT | Ling-3.1-flash-1019 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 60 | Ling-3.1-flash-1019 +5 |
| Risk | 20%20 | 70 | 45 | DiffusionGemma 26B A4B IT +25 |
| Agent interface | 10%10 | 70 | 60 | DiffusionGemma 26B A4B IT +10 |
| Documentation and upkeep | 10%10 | 60 | 56 | DiffusionGemma 26B A4B IT +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 51.3/100 | C 54.8/100 |
Facts side by side
| Fact | DiffusionGemma 26B A4B IT | Ling-3.1-flash-1019 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-15 | 2026-11-04 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.5/5 (4) | none |
How the scores have moved
Ling-3.1-flash-1019
No score change since it was listed.
Verdicts
DiffusionGemma 26B A4B IT
A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.
Ling-3.1-flash-1019
inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.
Before you call either
DiffusionGemma 26B A4B IT
- Serve it with
sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-codeon SGLang v0.5.22 or later, then call /v1/chat/completions - In Transformers, install
transformers>=5.11.0and loadDiffusionGemmaForBlockDiffusionwithdtype="auto"; generation_config.json already holds the recommended sampler - Turn thinking on with
enable_thinking=Truein apply_chat_template or<|think|>at the start of the system prompt, read the reasoning between<|channel>thoughtand<channel|>, and drop it from history before the next turn - 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
- Streaming arrives one 256-token canvas at a time, not token by token
- 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
Ling-3.1-flash-1019
- Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows
- With vLLM pass
--trust-remote-code,--tool-call-parser ling3and--reasoning-parser ling3 - Set
chat_template_kwargsenable_thinkingfalse for direct answers. Reasoning comes back inreasoning_contentper SGLang's cookbook - Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook
- Use temperature 1.0, top_p 0.95 and top_k 20, per the card
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, DiffusionGemma 26B A4B IT or Ling-3.1-flash-1019?
Ling-3.1-flash-1019 scores 54.8 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and agent interface.
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Machine-readable
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/compare/diffusiongemma-26b-a4b-it-vs-ling-3-1-flash-1019.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/diffusiongemma-26b-a4b-it.json·/api/v1/tools/ling-3-1-flash-1019.json - From a terminal
anchor compare diffusiongemma-26b-a4b-it ling-3-1-flash-1019(the CLI) - Over MCP
compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "ling-3-1-flash-1019"}at/mcp, no key