If you are building with OpenRouter and want a zero-cost starting point, the model list can feel noisy. Several free models look similar on paper, but their strengths differ a lot once you look at context limits, supported parameters, and input modalities.

This guide turns the latest public model metadata into a practical selection map for real engineering workflows.

Quick answer: which free model should you pick first?

If your main workflow is coding plus tool use, start with qwen/qwen3-coder:free.

If your main bottleneck is very long input context, start with deepseek/deepseek-v4-flash:free.

If you need stricter structured output control, test nvidia/nemotron-3-super-120b-a12b:free.

If you need image or video understanding, use google/gemma-4-31b-it:free.

If you want a lighter fallback for general agent tasks, try z-ai/glm-4.5-air:free.

1) Best default for coding agents: qwen/qwen3-coder:free

Why this is the safest first choice for code tasks:

  • The model is explicitly positioned for code generation, function calling, tool use, and long-context reasoning.
  • It supports a broad parameter set for agent workflows: tools, tool_choice, sampling controls, and token limits.
  • It has very large listed context, while still keeping a high provider context window for practical routing.

Use it for:

  • Repository Q&A and codebase reasoning
  • Multi-step implementation with tool calls
  • Refactors that need long source context

2) Best for huge-context ingestion: deepseek/deepseek-v4-flash:free

This model is strong when your prompt must carry very large documents.

Key practical advantage:

  • Both listed model context and provider context are currently at 1,048,576 tokens.

Tradeoff:

  • The supported parameter surface is narrower than some alternatives, so sampling/format-control knobs are more limited.

Use it for:

  • Large document summarization
  • Long transcript analysis
  • Broad-context reasoning where prompt size is the main constraint

3) Best for structured workflows: nvidia/nemotron-3-super-120b-a12b:free

This is a good fit when output discipline matters more than raw prompt size.

Why:

  • Supports response_format, structured_outputs, and seed
  • Also supports reasoning flags and tool calls for agent-style execution

Use it for:

  • JSON-first responses
  • Schema-sensitive pipelines
  • Multi-agent chains where deterministic structure matters

4) Best free multimodal option: google/gemma-4-31b-it:free

In this model set, Gemma is the practical pick when text alone is not enough.

Why:

  • Supports image and video input, unlike the other compared free options

Tradeoff:

  • Smaller max completion window than long-context text-focused models

Use it for:

  • Visual prompt understanding
  • Screenshot-based assistant workflows
  • Video-informed analysis with text output

5) Best lightweight fallback: z-ai/glm-4.5-air:free

GLM 4.5 Air is useful when you want a compact general agent model with lower complexity.

Why:

  • Supports reasoning and tool use
  • Good context size for moderate multi-step tasks

Use it for:

  • General assistant automation
  • Medium-context tasks
  • Backup routing when heavier models are unavailable

Practical selection strategy

Use this order to reduce trial time:

  1. Start with qwen/qwen3-coder:free for coding and tool-based workflows.
  2. Switch to deepseek/deepseek-v4-flash:free when prompt length is the primary bottleneck.
  3. Switch to nvidia/nemotron-3-super-120b-a12b:free when you need tighter structured output.
  4. Use google/gemma-4-31b-it:free for image/video input cases.
  5. Keep z-ai/glm-4.5-air:free as a lightweight fallback route.

Important caveats before production use

  • Free model routing can change without notice.
  • Rate limits and availability can be less stable than paid routes.
  • Model-level context and provider-level context may differ; use provider context for conservative planning.
  • Declared parameter support does not guarantee output quality for your specific task.

For production decisions, run a small eval set for your exact workload instead of choosing from specs alone.

Source note

This comparison is based on OpenRouter public model metadata as checked on 2026-05-23 from:

  • https://openrouter.ai/api/v1/models