GPT-5.4 Nano vs GPT-6.1 Sol
OpenAI · US | OpenAI · US · Updated June 2026
Quick verdict
Both are OpenAI models. GPT-6.1 Sol is the newer, generally stronger default; reach for GPT-5.4 Nano when its lower price or a specific cost or latency profile matters more than the latest capabilities.
GPT-5.4 Nano and GPT-6.1 Sol are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. GPT-5.4 Nano is openAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. GPT-6.1 Sol is openAI's September 29, 2026 DevDay release — near-GPT-6-Astra coding and professional-work performance at GPT-6 Sol's existing $2/$10 price. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
- ▸Price: GPT-5.4 Nano is about 10× cheaper on input ($0.2/$1.25 per 1M tokens vs $2/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: GPT-6.1 Sol holds 2.6× more — 1.05M tokens (~1,575 pages) vs 400K (~600 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: GPT-6.1 Sol is the newer model by about 7 months (released September 29, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | GPT-5.4 Nano | GPT-6.1 Sol |
|---|---|---|
| Provider | OpenAI (US) | OpenAI (US) |
| Released | March 17, 2026 | September 29, 2026 |
| Context window | 400K (~600 pages) | 1.05M tokens (~1,575 pages) |
| Price (in/out) | $0.2/$1.25 per 1M tokens | $2/$10 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, image |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work
GPT-5.4 Nano
At $0.2/$1.25 per 1M tokens it undercuts GPT-6.1 Sol ($2/$10 per 1M tokens), and that gap compounds at volume.
Classification, extraction, ranking and sub-agent execution at scale
GPT-5.4 Nano
OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning — and it runs cheaper at $0.2/$1.25 per 1M tokens.
A 400K context in the smallest, fastest GPT-5.4 variant
GPT-5.4 Nano
GPT-5.4 Nano lists a 400K context in the smallest, fastest GPT-5.4 variant among its strengths; GPT-6.1 Sol does not.
Nearly matches flagship GPT-6 Astra on agentic coding, computer use, and professional work, OpenAI says, at roughly one-fifth Astra's price
GPT-6.1 Sol
GPT-5.4 Nano is comparatively weak here — a nano tier — not built for hard reasoning or frontier coding
Factual-accuracy error rate falls to 7.7% at low reasoning effort, down from GPT-6 Sol's 11.4%
GPT-6.1 Sol
OpenAI's September 29, 2026 DevDay release — near-GPT-6-Astra coding and professional-work performance at GPT-6 Sol's existing $2/$10 price — and it carries the larger 1.05M tokens context.
Cached input priced at $0.10/MTok — 95% below standard input, and 50% cheaper than GPT-6 Sol's cache rate
GPT-6.1 Sol
GPT-5.4 Nano is comparatively weak here — image input only — no audio or video
Lowest cost at scale
GPT-5.4 Nano
At $0.2/$1.25 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-6.1 Sol
Its 1.05M tokens window is about 2.6× larger than GPT-5.4 Nano's 400K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-5.4 Nano
At $0.2/$1.25 per 1M tokens it undercuts GPT-6.1 Sol, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6.1 Sol
Larger 1.05M tokens window fits more in one prompt.
Anyone whose priority is cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work
→ GPT-5.4 Nano
It is specifically built for that.
Anyone whose priority is nearly matches flagship gpt-6 astra on agentic coding, computer use, and professional work, openai says, at roughly one-fifth astra's price
→ GPT-6.1 Sol
That is its strongest area.
GPT-5.4 Nano: where it fits
OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. Released March 17, 2026 by OpenAI, it is built for cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, classification, extraction, ranking and sub-agent execution at scale, a 400K context in the smallest, fastest GPT-5.4 variant, and text and image input for cheap multimodal pipelines.
Its trade-offs are real: a nano tier — not built for hard reasoning or frontier coding, no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead), outclassed by GPT-5.4 and GPT-5.4 Mini whenever a task needs real depth, and image input only — no audio or video. At $0.2 in / $1.25 out per million tokens, it sits in the budget price band.
GPT-6.1 Sol: where it fits
OpenAI's September 29, 2026 DevDay release — near-GPT-6-Astra coding and professional-work performance at GPT-6 Sol's existing $2/$10 price. Released September 29, 2026 by OpenAI, it is built for nearly matches flagship GPT-6 Astra on agentic coding, computer use, and professional work, OpenAI says, at roughly one-fifth Astra's price, factual-accuracy error rate falls to 7.7% at low reasoning effort, down from GPT-6 Sol's 11.4%, cached input priced at $0.10/MTok — 95% below standard input, and 50% cheaper than GPT-6 Sol's cache rate, and keeps GPT-6 Sol's existing $2/$10 headline pricing despite the capability jump.
Its trade-offs: launched in place of a hoped-for GPT-6.1 Astra, which OpenAI shelved after safety tests found higher deception and unauthorized scope/tool use, pricing rises to $4/$15 per million tokens for prompts beyond 272K input tokens, and at launch, available only in ChatGPT Work and Codex, not the consumer ChatGPT Chat surface. At $2 in / $10 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Because GPT-5.4 Nano and GPT-6.1 Sol come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. GPT-6.1 Sol is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to GPT-6.1 Sol and drop down only with a concrete reason.
Want both GPT-5.4 Nano and GPT-6.1 Sol without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is GPT-5.4 Nano or GPT-6.1 Sol better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work while GPT-6.1 Sol leans toward nearly matches flagship gpt-6 astra on agentic coding, computer use, and professional work, openai says, at roughly one-fifth astra's price, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-5.4 Nano or GPT-6.1 Sol?
GPT-5.4 Nano is cheaper — $0.2/$1.25 per 1M tokens vs $2/$10 per 1M tokens, roughly 10× apart on input.
Which has the bigger context window?
GPT-6.1 Sol — 1.05M tokens vs 400K, about 2.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GPT-5.4 Nano to GPT-6.1 Sol?
Since both are OpenAI models, the newer one (GPT-6.1 Sol) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-5.4 Nano or GPT-6.1 Sol?
GPT-6.1 Sol — released September 29, 2026, about 7 months after GPT-5.4 Nano.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.