The open-weight models worth trying on NanoGPT right now
We are past the point where open-weight models are just the cheap fallback.
For a long time, the practical advice was simple: use the strongest closed frontier model for difficult work, then reach for open-weight models when cost mattered more than quality. That is no longer how I would test a new workflow on NanoGPT. For coding agents, long-context analysis, RAG, orchestration, and cost-sensitive production jobs, several open-weight models now belong in the first round of testing.
Four open-weight models are especially worth paying attention to right now: DeepSeek V4 Flash, GLM 5.2, MiniMax M3, and NVIDIA Nemotron 3 Ultra. Here is what each is good for, what to watch out for, and how to try them on NanoGPT.
The short version
If you only want the practical answer:
- DeepSeek V4 Flash is the cheap coding and agent workhorse.
- GLM 5.2 is the one to try when the task needs planning, not just completion.
- MiniMax M3 is useful when long context, tools, and image input all matter.
- Nemotron 3 Ultra is the NVIDIA-backed open-weight option with the clearest enterprise story.
There is no universal winner. The useful question is what you are routing: quick code edits, long-context work, image-grounded analysis, or an agent that may run for a while.
DeepSeek V4 Flash: low-cost coding and agents
DeepSeek V4 Flash is cheap and it codes well.
On NanoGPT, try:
It is especially interesting for:
- coding agents
- bug fixing
- repo analysis
- structured technical work
- high-volume prompts where output cost matters
Start with the non-thinking route for fast direct answers. Move to the thinking route when the model needs to plan, debug, or keep several constraints in view.
GLM 5.2: planning-heavy engineering work
GLM 5.2 is the serious planning candidate in this group. It is the model I would reach for when the hard part is not one clever answer, but keeping a repo-scale plan coherent over many steps.
On NanoGPT, try:
Try it on:
- architecture reviews
- larger refactors
- multi-file coding tasks
- agent workflows with tool use
- long-context technical analysis
The thinking route is the natural place to start for long-horizon coding or planning. The non-thinking route is better when you want a faster answer and the task does not need extra deliberation.
GLM 5.2 is not the cheapest option in this set, so compare it against DeepSeek V4 Flash and MiniMax M3 on your real prompts. If it saves retries on hard coding work, the higher per-token cost can still make sense. If the task is simple extraction or formatting, it is probably overkill.
MiniMax M3: long context and mixed inputs
MiniMax M3's draw is the mix: long context, tool use, and multimodal understanding in one model family.
On NanoGPT, try:
Good places to test it:
- long document analysis
- whole-repo summaries
- tool-heavy coding tasks
- image-grounded analysis where image input is available
- agent workflows that need a large amount of context
Use it when the job mixes a lot of context with tools or screenshots: code review with UI context, document-heavy analysis, or agents that need to keep several files and instructions in view.
The cost caveat is context. Cheap input tokens do not automatically make a long-context job cheap. A very large prompt plus a thinking-heavy answer can still spend real money. Test with the actual context sizes and output lengths you expect to use.
Nemotron 3 Ultra: U.S.-built open weights
Nemotron 3 Ultra is less about chasing the top coding benchmark and more about the package: open weights, long-context support, tool-calling support, and NVIDIA's enterprise ecosystem.
On NanoGPT, try:
This is a good fit for:
- RAG
- internal assistants
- orchestration
- coding support
- enterprise workflows where vendor comfort matters
For teams that prefer a U.S.-developed model, NVIDIA's ecosystem, or a more familiar enterprise vendor story, that package can matter more than winning every coding leaderboard.
How to choose
Here is a simple starting point:
| Need | Try first |
|---|---|
| Cheapest strong coding and agentic model | DeepSeek V4 Flash |
| Hard planning or repo-scale engineering | GLM 5.2 |
| Long context with tool use or image input | MiniMax M3 |
| U.S.-built enterprise/open-weight option | Nemotron 3 Ultra |
Then run your own comparison. Benchmarks are useful, but your prompt shape matters more than a leaderboard once you get close to the top tier. A model that wins a coding benchmark may still be worse for your support bot. A model that looks expensive may be cheaper if it finishes the task in one pass.
Caveats before you build around one model
- Open-weight is not always open source in the strict license sense. Check the model license before building a commercial product or redistribution flow around it.
- Context length changes cost. A million-token window is useful, but it is not a default setting you should blindly fill. Use long context when the model genuinely needs it.
- Thinking tokens are output tokens. Reasoning modes can be better on hard tasks, but they can also add latency and cost. Compare thinking and non-thinking variants side by side.
- Pick for the input you actually have. If your workflow depends on screenshots or other non-text inputs, choose a model that supports them instead of assuming every strong text model will.
Try them on NanoGPT
You can test these models from the NanoGPT model picker or through the OpenAI-compatible API.
Use the API base URL:
https://nano-gpt.com/api/v1
Generate an API key in NanoGPT, use it as a Bearer token, and pass one of the model IDs above to /chat/completions.
My default is to keep a small bench rather than crown one permanent winner: cheap coding to DeepSeek V4 Flash, planning-heavy work to GLM 5.2, long-context mixed-input jobs to MiniMax M3, and enterprise-friendly open-weight tests to Nemotron. Run the same few prompts through each and keep the one that actually earns its spot.
