OpenAI’s GPT-5.1 family is now live on the OpenRouter API as of November 13, 2025. If you care about reasoning efficiency or coding workflows, this is a useful incremental step rather than a reset. The headline: you can now hit GPT-5.1, GPT-5.1 Chat, GPT-5.1-Codex, and GPT-5.1-Codex-Mini directly from OpenRouter, with long context, adaptive reasoning, and straightforward pricing.
Quick overview: the four GPT-5.1 models on OpenRouter
All of these models accept text and images and return text. Context lengths and prices differ, and the intended roles are fairly clear:
| Model | Context length | Primary role | Input price (per 1M tokens) | Output price (per 1M tokens) |
|---|---|---|---|---|
| GPT-5.1 | 400k | Frontier general model with stronger reasoning | $1.25 | $10 |
| GPT-5.1 Chat | 128k | Fast chat / instant responses | $1.25 | $10 |
| GPT-5.1-Codex | 400k | Software engineering and agentic coding | $1.25 | $10 |
| GPT-5.1-Codex-Mini | 400k | Smaller, faster coding model | $1.50 | $6 |
The family runs with adaptive reasoning. The models decide how much thinking to do based on difficulty, so simple chat stays fast while harder multi-step tasks get more depth. This was already the story with GPT-5, but 5.1 tightens up the tradeoffs and makes the middle settings much more attractive for real workloads.
How much better is GPT-5.1 than GPT-5?
This is a 0.1 release, not a new species of model. That shows up in both the numbers and how it feels in practice:
- On medium reasoning settings, GPT-5.1 now outperforms GPT-5 on high reasoning on many benchmarks, while using less compute.
- On minimal reasoning, GPT-5.1 Chat is much stronger than the previous instant variant that barely thought at all.
- On some evals you see small drops, but they are within the noise; on others, you get a noticeable bump.
- There are clear improvements for SVG generation and frontend code, which matters if you build UIs with the model.
Roughly speaking, the version number went up by a couple of percent and so did capability. That is exactly what you would expect from a point release. If you want a deeper breakdown of reasoning modes and the Instant vs Thinking split, I covered that for the earlier ChatGPT-facing rollout here: GPT-5.1 Instant and Thinking: What I’m Actually Watching.
Pricing: Codex-Mini is the cheap workhorse
Pricing across the 5.1 family is simple. Input is basically flat; output is where Codex-Mini stands out.
Output tokens are where GPT-5.1-Codex-Mini is noticeably cheaper than the other GPT-5.1 models on OpenRouter.
If you are optimizing for cost at scale and can accept a slightly smaller coding model, GPT-5.1-Codex-Mini is the obvious default. For general-purpose or high-stakes work, stick with full GPT-5.1 or GPT-5.1-Codex and treat Codex-Mini as the high-volume helper rather than the main engineer.
What changed for Codex and coding work
The biggest practical shift for developers is that I no longer see a reason to stay on GPT-5-Codex by default. We now have:
- GPT-5.1-Codex for heavy engineering sessions, autonomous agents, and large refactors.
- GPT-5.1-Codex-Mini for frequent small edits, debugging, and high-volume runs where cost matters.
Benchmarks for the new Codex models are still thin, so I am not going to claim that GPT-5.1-Codex-Mini is definitively better than GPT-5-Codex-Mini yet. The early data that does exist points to a small reduction in token usage and modest eval gains, which is exactly what you would expect from a targeted fine-tune on top of GPT-5.1.
There are also practical quality-of-life shifts: better SVG output, cleaner frontend snippets, and stronger multi-step reasoning when the model has to coordinate changes across multiple files. That matters more than a single headline benchmark if you are asking it to touch a real codebase instead of a toy exercise.
What is clear is that the Codex ecosystem is now even more confusing from a naming perspective. If you count everything OpenAI calls Codex, you get twelve separate things:
- Products / tools: OpenAI Codex 2021 API, Codex CLI, Codex cloud agent, Codex IDE extension, Codex SDK.
- Models: the original 2021 Codex model, codex-mini-latest, codex-1, GPT-5-Codex, GPT-5-Codex-Mini, GPT-5.1-Codex, GPT-5.1-Codex-Mini.
If that naming scheme bothers you, you are not alone. But from a practical perspective, the move that matters today is simple: if you are coding against OpenRouter and you want OpenAI’s stack, switch your main coding calls to GPT-5.1-Codex or GPT-5.1-Codex-Mini and treat the older GPT-5 Codex variants as legacy unless you have a reason not to move.
If you care about how these compare against non-OpenAI coding options, I also wrote about other strong engineering-focused models such as Minimax M2 and GLM 4.6 here: Minimax M2 vs GLM 4.6: Coding Powerhouses Compared on Cost, Speed, and Capabilities, and about free agentic options like Kat Coder here: Kwaipilot’s Kat Coder: A Free Agentic Coding Model with a 73.4% SWE-Bench Score.
No Mini or Nano general GPT-5.1 models yet
One interesting gap: OpenAI shipped GPT-5.1-Codex-Mini, but there is still no GPT-5.1 Mini or GPT-5.1 Nano general model on OpenRouter. Given that Codex-Mini exists, it is reasonable to assume the same techniques will show up in the smaller general-purpose models. For now, though, you either pay for the full frontier 5.1 stack or you stay on older or third-party small models.
On the competitive side, this drop slots into the broader race between OpenAI, Anthropic, and Google. I wrote separately about why I expect Gemini 3 Pro to pull ahead of GPT-5.1 on some fronts in this piece: The AI Model Rush: Why Gemini 3 Pro Will Lead the Pack Against GPT-5.1 and Claude Opus 4.5. GPT-5.1 on OpenRouter is another data point in that race, but not the finish line.
Which GPT-5.1 model should you actually use?
If you just want a direct answer on model selection, here is how I would think about it right now:
- Use GPT-5.1 for long-context reasoning, mixed workloads, and anything that looks like general AI assistant work with high quality expectations.
- Use GPT-5.1 Chat for low-latency chatbots, support tools, or user-facing features where snappy responses matter more than squeezing out the last bit of accuracy.
- Use GPT-5.1-Codex for serious coding agents, repo-wide refactors, code review pipelines, and long-running engineering tasks.
- Use GPT-5.1-Codex-Mini anywhere you need lots of cheap, frequent code edits and debugging steps with reasonable quality.
The key improvement is not that GPT-5.1 suddenly makes everything else obsolete. It is that medium reasoning on GPT-5.1 hits a better quality and cost balance than high reasoning on GPT-5, and the instant/chat path is no longer obviously hobbled. That matters if you are running real workloads, not just benchmarks.
Practical rollout tips for teams using OpenRouter
If you are already on OpenRouter, updating to GPT-5.1 is mostly about swapping model names and tuning reasoning settings. A simple approach:
- Keep your high-value, mixed tasks on GPT-5.1 with a medium reasoning setting as the new default.
- Move chat-style UX to GPT-5.1 Chat with minimal reasoning, then selectively turn up reasoning only for branches that actually need deep thinking.
- Shift coding agents and CI-style checks to GPT-5.1-Codex and reserve GPT-5.1-Codex-Mini for bulk edits and quick-fix suggestions.
- Monitor token usage and error rates for a week or two instead of trusting a single benchmark screenshot. The gains here are mostly about efficiency over time.
If you care about token efficiency tricks more broadly, I also wrote about TOON as a structured format that beats JSON on size by a wide margin: How TOON Cuts Token Usage by Up to 60% Compared to JSON for LLMs. GPT-5.1’s better reasoning balance pairs well with formats that waste fewer tokens.
The simple takeaway: GPT-5.1 on OpenRouter is a solid upgrade, especially if you care about efficiency and coding. Update your API calls, pick the right variant for each job, and then watch for the smaller GPT-5.1 Mini and Nano models if and when they show up.
