ModelFoldgpt-6-1-sol
GPT-6.1 Sol is OpenAI's upgrade to GPT-6 Sol, positioned between GPT-6 Astra and GPT-6 Luna, with a 1,050,000-token context window.
Pricing: Input 150 credits / 1M tokens (≈ $0.75), Output 750 credits / 1M tokens (≈ $3.75).
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GPT-6.1 Sol API: How It Compares with GPT-6 Astra and Luna
GPT-6.1 Sol is OpenAI's upgrade to GPT-6 Sol, positioned between GPT-6 Astra and GPT-6 Luna. OpenAI describes it as nearly matching Astra on agentic coding, computer use and professional work at a lower price than Astra, and it has a 1,050,000-token context window. On ModelFold it is available alongside GPT-6 Astra and GPT-6 Luna.
GPT-6.1 Sol API Parameters on ModelFold
| Parameter | Value |
|---|---|
| Model | gpt-6-1-sol |
| Endpoint | POST https://ai.modelfold.online/v1/chat/completions (OpenAI-compatible) |
| Context window | 1,050,000 tokens |
| Maximum input | 922,000 tokens |
| Maximum output | 128,000 tokens |
| Input types | Text messages |
| Reasoning effort | low, medium, high, xhigh or max (adjustable in the Playground) |
| Tools | Web search (available in the Playground) |
| Streaming | Not streamed; the API returns one JSON response with usage and credits charged |
| Pricing | Shown in the pricing card above |
GPT-6.1 Sol vs GPT-6 Astra vs GPT-6 Luna: Which One to Use
On OpenAI's own evaluations, GPT-6.1 Sol matches GPT-6 Astra on DeepSWE v1.1 and comes within 2.1 points of Astra on OSWorld 2.0 at maximum reasoning effort, while Astra scored above GPT-6.1 Sol on Terminal-Bench Science 0.1, at 68.1%. Independent leaderboards also put the two close: Artificial Analysis gives Astra 53 and Sol 52 on its Intelligence Index, and LMArena's text leaderboard gives Sol 1484 and Astra 1475 with overlapping ranges (as of October 2026). GPT-6 Luna scores 38 on the same Artificial Analysis index. A practical default is Sol for coding, computer-use and document work, with Astra for the hardest research tasks, which is where OpenAI recommends Astra. On ModelFold, Sol costs less than Astra; each model's pricing card has the details.
| GPT-6.1 Sol | GPT-6 Astra | GPT-6 Luna | |
|---|---|---|---|
| How OpenAI positions it | Nearly matches Astra on agentic coding, computer use and professional work | Its best model across the board, for when you want the best results | A faster, more affordable GPT-6 model trained with Astra's methods |
| Artificial Analysis Intelligence Index (Oct 2026) | 52 | 53 | 38 |
| LMArena text score (Oct 8, 2026) | 1484 | 1475 | 1443 |
| OpenAI-reported comparison with Astra | Matches Astra on DeepSWE v1.1; within 2.1 points on OSWorld 2.0 (max effort) | Scored above GPT-6.1 Sol on Terminal-Bench Science 0.1 (68.1%) | Not reported in the GPT-6.1 Sol release |
| Available on ModelFold | Yes | Yes | Yes |
What GPT-6.1 Sol Improves Over GPT-6 Sol
OpenAI built GPT-6.1 Sol as an upgrade to GPT-6 Sol for agentic coding, computer use and professional work such as understanding documents and running multi-step business workflows. These are the results OpenAI reports against GPT-6 Sol.
| Benchmark (OpenAI-reported) | What it measures | GPT-6.1 Sol vs GPT-6 Sol |
|---|---|---|
| DeepSWE v1.1 | Complex software-engineering tasks in real codebases | 6.4 points above GPT-6 Sol's best score, at a lower reasoning effort |
| AutomationBench | Multi-step business workflows | 4.8 points higher at medium reasoning effort |
| OSWorld 2.0 (offline set) | Demanding computer-use workflows | 7 points higher at maximum reasoning effort |
| Terminal-Bench Science 0.1 | Data analysis, simulation and theorem proving | More than double GPT-6 Sol's score at maximum reasoning effort |
| Factuality evaluation | Share of answers with at least one factual error | 11.4% to 7.7% at low reasoning effort, on deliberately difficult prompts that are not representative of typical usage |
Typical Uses for GPT-6.1 Sol
OpenAI built GPT-6.1 Sol for complex professional work. These are the kinds of tasks it was built for.
Coding Agents and Code Review
Writing, debugging and reviewing code in real codebases, where an agent works through complex software-engineering tasks.
Answers From Complex Documents
Questions answered from long PDFs with tables, charts, diagrams and fine print.
Multi-Step Business Workflows
Agents that carry a business task through several steps across different apps.
Computer-Use Agents
Agents that operate computer applications to complete multi-step tasks.
How to Use the GPT-6.1 Sol API on ModelFold
- 1
Step 1. Get Your API Key
Sign up on ModelFold, open the API Key Management page and create a key. Every request sends it in the Authorization header.
- 2
Step 2. Test Your Prompt in the Playground
You can try this model in the playground on this page without writing code. Try a few reasoning effort levels to see how the answers change.
- ✓
Step 3. Send a Request
Send a POST request to https://ai.modelfold.online/v1/chat/completions with model set to gpt-6-1-sol and an OpenAI-style messages array, authenticated with your API key.
Tips Before You Ship
Handle Standard HTTP Errors
ModelFold returns standard HTTP status codes — for example 401 for an invalid API key and 402 for insufficient credits — with an error code and message in the JSON body. Failed upstream calls are refunded automatically.
Try Web Search in the Playground
Web search and reasoning effort for this model can be switched on in the Playground on this page, so you can see how they change the answers before you integrate.
Keep the Model ID Configurable
Requests and responses both use the id gpt-6-1-sol. Keeping it in configuration makes it easy to switch to gpt-6-astra or gpt-6-luna later without code changes.
Why Use GPT-6.1 Sol on ModelFold
Same Key Across the ModelFold Market
All models in the ModelFold market use the same authentication method: an Authorization: Bearer YOUR_API_KEY header with the key from your ModelFold API Key Management page.
Free Credits to Start
New ModelFold users receive 80 free credits for testing. Your account has one credit balance that every model draws from, and you can see it, together with every charge, in your wallet.
Nothing to Host or Install
ModelFold provides this model as an API with a playground; there is no app to download or install. There are no GPUs or model weights to manage.
Frequently Asked Questions
Is GPT-6.1 Sol better than GPT-6 Astra?⌄
Not across the board. On OpenAI's evaluations it matches Astra on some coding and computer-use work, and independent leaderboards score the two within a few points, while OpenAI still recommends Astra for the most difficult research tasks. The comparison table above has the scores.
What is the difference between GPT-6.1 Sol and GPT-6 Luna?⌄
On the Artificial Analysis Intelligence Index, GPT-6 Luna scores 38 and GPT-6.1 Sol scores 52 (as of October 2026). All three GPT-6 models are available on ModelFold, and the comparison table above lines them up.
What is GPT-6.1 Sol?⌄
GPT-6.1 Sol is OpenAI's upgrade to GPT-6 Sol, built for agentic coding, computer use and professional work. On ModelFold it is the API model gpt-6-1-sol; its parameters, scores and uses are covered above.
Is GPT-6.1 Sol better than GPT-6 Sol?⌄
Yes, on the results OpenAI reports: it scores higher on coding, business-workflow, computer-use and science benchmarks and makes fewer factual errors on difficult prompts. The benchmark table above lists each result.
How much does the GPT-6.1 Sol API cost?⌄
Pricing is shown in the pricing card above.
What is the GPT-6.1 Sol context window?⌄
OpenAI lists a 1,050,000-token context window with up to 128,000 output tokens. The parameter table above has the other limits.
Is GPT-6.1 Sol free?⌄
Not on ModelFold: each request uses credits, as shown in the pricing card above. New ModelFold users receive 80 free credits for testing.
What reasoning effort levels does GPT-6.1 Sol support?⌄
Low, medium, high, xhigh and max, with medium as the default.
| Input | 150 credits 1M tokens≈ $0.75 |
| Output | 750 credits 1M tokens≈ $3.75 |
Billed per token: input and output are priced separately, and thinking tokens count as output. The Playground charges exactly the same way.
Read API docs →curl https://ai.modelfold.online/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-6-1-sol","messages":[{"role":"user","content":"Hello"}]}'