Ollama Release Notes

Clef decision models

Ollama now supports Clef and Clef Flash, Cloudflare's new open-source decision models, through /v1/systemone.

Clef (27B) and Clef Flash (9B) are multimodal: requests can now include images alongside the text state, shared by all questions and scored jointly with it.

curl http://localhost:11434/v1/systemone -d '{
  "model": "clef-flash",
  "state": "The user took this screenshot.",
  "images": ["<base64-encoded image>"],
  "questions": {
    "has_ollama": {"type": "noul", "instructions": "Does this image contain Ollama?"}
  }
}'

{
  "model": "clef-flash",
  "answers": {
    "has_ollama": {
      "type": "noul",
      "noul": 0.958
    }
  },
  "usage": {
    "input_tokens": 548,
    "output_tokens": 0
  }
}

What's Changed

• Models using web search can now perform up to ten searches per response, up from three
• Modelfiles now support CAPABILITY declarations, so model creators can explicitly declare what a model can do. Declarations are preserved when creating from GGUF or safetensors, through model inheritance, and on Modelfile export
• ollama show and the model list now report only decision as the capability for decision models, so clients no longer offer them for general chat, tools, or thinking
• Updated llama.cpp and the MLX engine

Full Changelog: https://github.com/ollama/ollama/compare/v0.35.0...v0.35.1

Decision models

Ollama now supports decision models through /v1/systemone, based on TypeSafe’s Jev API.

Decision models return choices, probabilities, and scores instead of text. Use them for tasks such as ticket triage, model routing, and content classification.

Available models:

• Nimble from Bespoke Labs
• Tev1 from Together AI

ollama pull nimble

Send context and one or more questions:

curl http://localhost:11434/v1/systemone \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "nimble",
    "state": "Our checkout has returned 500 errors since 9am.",
    "questions": {
      "label": {
        "type": "choice",
        "instructions": "Which label fits this ticket?",
        "criteria": {
          "billing": "Payments and refunds",
          "bug": "Software errors",
          "account": "Login and account access"
        }
      }
    }
  }'

Example response:

{
  "model": "nimble",
  "answers": {
    "label": {
      "type": "choice",
      "choice": "bug",
      "probabilities": {
        "billing": 0.0125,
        "bug": 0.9781,
        "account": 0.0093
      },
      "confidence": 0.8906
    }
  },
  "usage": {
    "input_tokens": 174,
    "output_tokens": 1
  }
}

The API supports three question types: …

What's Changed

Models run on MLX on Apple Silicon by default

In this release, on Apple Silicon devices, model architectures supported by the MLX runtime will automatically run on MLX.

ollama pull qwen3.8
ollama run qwen3.8

Additional models include gemma4, qwen3.6 and qwen3.5

Decision models are now available on MLX as well: Nimble tev1 clef clef-flash

MLX now has support for an embedding model: embeddinggemma-2

We will continue testing and enabling additional models.

Full Changelog: https://github.com/ollama/ollama/compare/v0.35.1...v0.40.0

What's Changed

• Structured outputs on thinking models now apply in a single pass, making them faster and more reliable.
• Fixed intermittent "model not found" errors with a large local library
• Fixed the macOS app becoming unresponsive when checking if ChatGPT or Codex is running.
• Qwen 3.8 prompt processing is faster on Apple Silicon.
• Gemma 4 on Apple Silicon now picks the best image resolution per image, keeping more detail in high-resolution images.
• Updated llama.cpp, MLX, and XGrammar.

Full Changelog: https://github.com/ollama/ollama/compare/v0.34.3...v0.34.4

What's Changed

GET /api/show now advertises each model's thinking controls and default:

Available in the CLI with:

ollama show gemma4

    thinking
        levels     false, true
        default    true

Available in the API with:

curl http://localhost:11434/api/show -d '{"model": "glm-5.3-flash:cloud"}'

{
  "thinking": {
    "values": ["low", "high", "max"],
    "default": "max"
  }
}

Also available on ollama.com directly for cloud models.

• Nemotron H vision models are now supported on Apple Silicon with MLX
• Ollama's macOS app will now no longer reopen windows you've closed when activating it
• Fix for model pulls from HuggingFace

Full Changelog: https://github.com/ollama/ollama/compare/v0.34.2...v0.34.3

What's Changed

• MLX safetensors ollama create no longer experimental. GGUF model creation now requires using llama.cpp tooling for safetensor conversion and quantization.
• Improved MLX memory handling on Apple Silicon
• Runaway repeat token detection now requires 100 repeat tokens for reduced false positives (e.g. OCR)
• /api/tags is much faster on large model libraries (3.1 s → 294 ms cold in testing), and model capabilities are now reported consistently.
• Deprecated typical_p: it can no longer be set when creating new models, existing GGUF models retain support.
• MLX and llama.cpp updates

Full Changelog: https://github.com/ollama/ollama/compare/v0.34.0...v0.34.1-rc1

Use Ollama models in ChatGPT Desktop

Ollama models can now be used directly in ChatGPT Desktop, so you can keep your existing workflow while running open models. Setup is available from the Ollama app on MacOS.

<img width="1374" height="1300" alt="CleanShot 2026-09-08 at 11 04 07 AM@2x" src="https://github.com/user-attachments/assets/e10e299d-c11f-447d-9234-afa855824efe" />

This release also improves structured output performance on Apple Silicon, adds support for OpenAI-compatible client tool search and response compaction.

Full Changelog: https://github.com/ollama/ollama/compare/v0.33.3...v0.34.0

What's Changed

• MLX: Qwen3.8 Flash Next support
• cmake: make external compat patches idempotent
• MLX and llama.cpp update
• mlxrunner: add structured output support
• mlxrunner: avoid Metal GPU timeouts when loading models from slow storage

New Contributors

• @pd95 made their first contribution in https://github.com/ollama/ollama/pull/17948

Full Changelog: https://github.com/ollama/ollama/compare/v0.33.0...v0.33.1

What's Changed

Claude Desktop

Developers can now easily configure Claude Desktop to seamlessly work with Ollama as a third-party gateway provider.

<img width="1298" height="780" alt="image" src="https://github.com/user-attachments/assets/c02fd93b-f599-40a9-83c3-d0555c0ac182" />

Improved caching

• Fixed a hang where agent clients that cancel long prefills
• Prefill restore points are now trustworthy by construction: a cancelled prefill keeps every restore point it crossed, so retries resume where they stopped instead of restarting from scratch
• Resumed prefills no longer record restore points that fail to cover what they claim; on models with recurrent layers this previously forced a request matching 46k of 47k tokens to reprocess from zero
• Disabled Claude Code's "tokens left" token-countdown system message, which Ollama moved to the front of the prompt and broke the KV cache on every request

Other improvements

• DeepSeek Harness launcher now falls back to npx when the global npm install fails, with Windows command-shim support
• MLX dependency update (#17886)
• Fixed broken default packaging caused by macOS-specific assumptions affecting Linux/Windows builds

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.15...v0.33.0

What's Changed

• New desktop onboarding flow on first launch
• Caches resolved model metadata between requests, cutting time-to-first-token by roughly half (TTFT dropped from ~995 ms to ~524 ms in benchmarks)
• Fixes a bug where chat and generate could wedge after a mid-stream parser error
• Qwen 3.8 system messages are now normalized so non-leading system messages are handled consistently
• MLX and llama.cpp dependency updates

New Contributors

• @gaugarg-nv made their first contribution in https://github.com/ollama/ollama/pull/17752

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.14...v0.32.15

What's Changed

• ollama launch dsh now supports DeepSeek Harness, DeepSeek's open-source agent harness
• ollama launch muse now supports Muse Code, Meta's agentic coding CLI
• The OpenAI-compatible Responses API now supports web search
• Muse Glimmer template updates

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.10...v0.32.11

What's Changed

• Models that don't set a repeat_penalty now default to 1.0 (off) instead of 1.1, matching other engines and speeding up speculative decoding; set a per-model parameter if an older model repeats itself.
• Faster prefill on NVFP4 MLX models with a global scale, about 7–8% on Qwen3.6 and Muse Glimmer.
• Fixed blob verification being skipped when an OCI manifest's config and layer share a digest.

New Contributors

• @vigneshakaviki made their first contribution in https://github.com/ollama/ollama/pull/15504

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.8...v0.32.10-rc1

NVIDIA Nemotron 3.5 Lightning

NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts (MoE) model with 3B active parameters built for that execution layer of always-on agents. It is designed for harnesses like OpenClaw and Hermes Agent – all supported by the NVIDIA NemoClaw open source security and management stack for running always-on AI agents.

ollama run nemotron-3.5-lightning

What's Changed

• Added the Nemotron 3 architecture
• Handle boundary condition in Muse Glimmer function calling parser

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.8...v0.32.9

Muse Glimmer

Muse Glimmer is now available on all platforms. Muse Glimmer can power coding agent applications such as Claude Code, Codex, Pi and more, as well as long-running personal assistants such as OpenClaw and Hermes.

Ollama's MLX engine provides state-of-the-art performance on Apple Silicon for this model, with support for DFlash and image input as of Ollama 0.32.7.

To download and run Muse Glimmer locally:

ollama run muse-glimmer

To run Muse Glimmer with Claude Code, download Ollama and run:

ollama launch claude --model muse-glimmer

For a lighter-weight coding agent, try Pi:

ollama launch pi --model muse-glimmer

For personal assistant frameworks such as OpenClaw and Hermes, use:

ollama launch openclaw --model muse-glimmer

ollama launch hermes --model muse-glimmer

What's Changed

• Add Muse Glimmer support for NVIDIA, AMD, and additional platforms

Full Changelog: https://github.com/ollama/ollama/compare/v0.32.7...v0.32.8