Oct 2, 2026

GPT-6.1 Sol as a Cursor and Cline Backend: Near-Astra Coding at One-Fifth the Price?

Q: What is the exact model ID for GPT-6.1 Sol?

`gpt-6.1-sol` on the OpenAI API (gateways list it as `openai/gpt-6.1-sol`). If a tool's picker doesn't show it, enter the ID manually through an OpenAI-compatible provider config.

By AICoderScope Team · 13 min read

openaigpt-6cursorclinebyokpricingcost-analysisapi

TL;DR: GPT-6.1 Sol (released September 29, 2026) costs $2/$10 per million tokens — one-fifth of GPT-6 Astra and Claude Fable 5.1, both $10/$50 — and matches Astra on OpenAI’s own DeepSWE v1.1 coding eval (75.22% at high effort vs Astra’s 74.1% best run). The catch: requests over 272K input tokens are repriced 2x input / 1.5x output for the entire request, and every benchmark is still vendor-reported. For budget-capped agentic coding in Cursor or Cline, it is the default pick today.

	GPT-6.1 Sol	GPT-6 Astra	Claude Fable 5.1	Claude Sonnet 5.5
Input / output per MTok	$2 / $10	$10 / $50	$10 / $50	$2 / $10
Cached input per MTok	$0.10	—	$0.25	$0.20
Context window	1.05M (2x price >272K input)	—	1M, flat price	1M, flat price
DeepSWE v1.1 (vendor-run)	75.22% (high effort)	74.1% (best run)	not on this eval	not on this eval
The catch	long-context surcharge; no independent evals yet	5x the price for ~equal DeepSWE score	5x price; strongest on accuracy-critical refactors	similar price, smaller output ceiling in agent loops

Honest take: If your agent sessions stay under ~270K input tokens per request, GPT-6.1 Sol is the best price-to-capability coding backend you can put in Cursor or Cline right now — take it over Astra, and trial it against your Fable 5.1 workflows before renewing anything. If your sessions routinely blow past 272K context, the one-fifth claim quietly becomes one-half, and Claude’s flat 1M-context pricing deserves the rematch.

What is GPT-6.1 Sol and what does it cost?

GPT-6.1 Sol is OpenAI’s mid-tier frontier model, released September 29, 2026, positioned as “near-Astra intelligence at one-fifth the price.” The API model ID is gpt-6.1-sol, and pricing is $2 per million input tokens, $10 per million output tokens, and $0.10 per million cached input tokens — the same rates as GPT-6 Sol, which it supersedes as the workhorse tier (GPT-6 Sol itself had replaced GPT-5.6 Sol at half its price on September 22).

The headline specs, verified October 2, 2026:

Context window: 1,050,000 tokens, with a 128,000-token output ceiling
Long-context surcharge: any request with more than 272,000 input tokens is billed at 2x input and cache rates and 1.5x output — applied to the entire request, not just the overage
Effort levels: low / medium (default) / high / xhigh / max — OpenAI’s benchmark numbers were run at high and max
Modality: text and image input, text output

That surcharge structure is the single most important line in the pricing table, and we’ll come back to it, because it is where the one-fifth marketing claim stops being true for a specific class of agentic workloads.

For comparison, Claude Fable 5.1 — the model Sol is implicitly priced against — runs $10/$50 with $0.25/MTok cache reads, per Anthropic’s official pricing page as of today. GPT-6 Astra, OpenAI’s own flagship from September 3, is also $10/$50.

How good is GPT-6.1 Sol at coding, really?

On OpenAI’s own evals it slightly beats Astra on software engineering while costing roughly 85% less per completed task — but every one of these numbers is vendor-run, and no independent replication had been published as of October 2, 2026. With that flag planted, here is what OpenAI reported:

Benchmark (OpenAI-run)	GPT-6.1 Sol	GPT-6 Astra	Cost per task (Sol vs Astra)
DeepSWE v1.1 (software engineering)	75.22% (high effort)	74.1% (best run)	$0.65 vs $4.43
OSWorld 2.0 (computer use)	71.42% (max effort)	~2.1 pts higher	~1/7 the cost
Terminal-Bench Science 0.1	57.02% (max effort)	—	more than 2x its predecessor’s score

Three things to keep straight when reading that table. First, DeepSWE is OpenAI’s harness — when GPT-5.6 Sol launched in July 2026 claiming a 2.8-point lead over Claude Fable 5 on the Artificial Analysis Coding Agent Index, the number came from OpenAI’s own setup and was argued about for weeks. Treat 75.22% the same way until Artificial Analysis or SWE-bench posts third-party runs. Second, the Sol scores above were achieved at high and max effort, which burn more output tokens than the medium default; the $0.65-per-task figure already accounts for that, but your Cursor sessions at default effort will not reproduce benchmark behavior. Third, there is no published Claude comparison on DeepSWE v1.1 — the honest statement is “Sol ≈ Astra on OpenAI’s eval,” not “Sol beats Claude.”

What the benchmarks do establish with reasonable confidence: Sol is in the same working tier as the $10/$50 flagships for multi-step coding agent work, at token prices that match Claude Sonnet 5.5 ($2/$10). That alone changes the BYOK math.

How do you set up GPT-6.1 Sol in Cursor?

Add your OpenAI API key under Settings → Models → API Keys, enable the OpenAI key toggle, and select gpt-6.1-sol — but know what BYOK does and does not cover in Cursor before you cancel anything. Per Cursor’s own forum guidance (verified October 2026):

A custom OpenAI key (and the Override OpenAI Base URL field) applies to Chat and Agent requests only.
Tab autocomplete always runs on Cursor’s own proprietary model and is billed through your Cursor subscription regardless of BYOK settings. You cannot point Tab at Sol or any custom endpoint.
If gpt-6.1-sol doesn’t appear in your picker yet, add it as a custom model name — Cursor passes the string through to the OpenAI API.

Sanity-check the key and model ID from a terminal before blaming Cursor:

curl -s https://api.openai.com/v1/responses \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-6.1-sol", "input": "Reply with OK"}' | head -c 300

A working key returns a JSON object whose "model" field echoes gpt-6.1-sol and whose output contains OK. A model_not_found error means your account doesn’t have access (check your tier at platform.openai.com) — not a Cursor problem.

The practical split most Cursor users land on: keep the $20/mo Pro plan for Tab and the occasional included-model request, and route long agentic sessions through the Sol key, where flat-rate request limits would otherwise bite.

How do you set up GPT-6.1 Sol in Cline?

Use Cline’s OpenAI Compatible provider with base URL https://api.openai.com/v1, API key, and model ID gpt-6.1-sol typed manually — this works today even where the native OpenAI provider’s model catalog hasn’t refreshed. We have seen this catalog-lag pattern repeatedly (Cline’s picker was still missing Claude Opus 5.5 weeks after launch, as we documented in the Opus 5.5 backend guide), so don’t wait on the dropdown:

{
  "apiProvider": "openai",
  "openAiBaseUrl": "https://api.openai.com/v1",
  "openAiModelId": "gpt-6.1-sol",
  "openAiModelInfo": {
    "contextWindow": 272000,
    "maxTokens": 128000,
    "supportsImages": true
  }
}

Note the deliberate contextWindow: 272000 instead of the model’s real 1,050,000. That is a cost guardrail, not a typo — the next two sections explain why.

One real-world failure mode from the first week: OpenAI’s own Codex VS Code extension shipped with gpt-6.1-sol missing from its model picker while the same accounts saw it in the Codex app and CLI (tracked in openai/codex issue #49464). If a first-party extension can lag its own launch, assume third-party pickers will too. The fix in every tool is the same: enter the model ID string manually via the OpenAI-compatible path, and confirm with the curl test above that your account actually has API access.

What does a real agentic session cost: Sol vs Claude Fable 5.1?

About $0.93 versus $4.58 — Sol is roughly 4.9x cheaper on a representative cached Cline session, as long as no single request crosses the 272K line. The math, using official October 2 pricing for both models:

A typical mid-size Cline refactor session: 500K total input tokens across the loop (300K of them cache reads on later turns), 50K output tokens.

Line item	GPT-6.1 Sol	Claude Fable 5.1
200K fresh input	200K × $2/M = $0.40	200K × $10/M = $2.00
300K cached input	300K × $0.10/M = $0.03	300K × $0.25/M = $0.08
50K output	50K × $10/M = $0.50	50K × $50/M = $2.50
Per session	$0.93	$4.58
60 sessions/month (3/day × 20 workdays)	~$56	~$275

At that volume the delta is ~$219/month — more than ten Cursor Pro subscriptions. For a solo developer running heavy agentic work on BYOK, this is the difference between “API bill is noise” and “API bill is my largest tool expense.” Our Opus 5 default cost analysis found the same shape when Claude Code switched defaults: output-token rates dominate agentic costs, and Sol’s $10/M output vs Fable’s $50/M is where 80% of the gap lives.

Boundary conditions, stated plainly: this favors Sol only if quality is actually interchangeable for your tasks. On accuracy-critical multi-file refactors, where a failed run costs you review time rather than tokens, a model that is right the first time is cheaper at 5x the token price — and there is no independent eval yet showing Sol matches Fable 5.1 there. Run both on your own repo for a week before moving standing workflows.

Where does the one-fifth price claim break down?

Above 272,000 input tokens in a single request, Sol’s entire request is repriced to $4/$15 effective rates, and the gap to Claude collapses from 5x to about 2.6x. This is the trap the Cline config above guards against, and the cliff is sharper than it sounds: a 272K-input request costs $0.54 in input tokens, while a 273K-input request costs $1.09 — crossing the line by a few thousand tokens doubles the input bill for everything before it. Worse, agent loops only grow context, so once one turn of a session crosses 272K, every subsequent turn inherits the surcharge.

Compare a single 300K-input, 10K-output request:

GPT-6.1 Sol (surcharged): 300K × $4/M + 10K × $15/M = $1.35
Claude Fable 5.1 (flat): 300K × $10/M + 10K × $50/M = $3.50

Anthropic’s pricing page is explicit that Claude 4.6-and-later models bill a 900K-token request at the same per-token rate as a 9K one — no cliff anywhere in the 1M window. Sol is still cheaper above the cliff, but 2.6x is not the one-fifth on the box, and cache reads double too ($0.20/M), eroding the long-session advantage further.

Three mitigations that work today: cap the advertised context window in your tool config (as in the Cline JSON above) so the agent summarizes instead of accumulating; use Cline’s auto-compact / context-condensing behavior aggressively on long sessions; and split repo-scale analysis into scoped subtasks rather than one mega-context run. If your work genuinely needs 400K+ of live context per request — monorepo archaeology, giant log forensics — price Claude Sonnet 5.5 ($2/$10 flat to 1M) against surcharged Sol before defaulting to either flagship.

Which developers should switch, and which should hold?

Switch now — solo dev, budget-capped agentic coding: GPT-6.1 Sol wins. Same sticker price as Sonnet 5.5, near-flagship vendor benchmarks, $10/M output instead of $50/M. Wire it into Cline via OpenAI Compatible and keep sessions compacted. If your API spend was the reason you were eyeing local models, Sol at ~$1/session also resets that math — though a used-GPU Ollama stack still wins at zero marginal cost if the hardware is sunk; see the best local coding LLM guide on runaihome.com for what 24GB of VRAM buys you, and the Ollama review on aifoss.dev for the serving layer.

Switch, with a one-week trial — teams standardizing a Cursor/Cline backend: route code review and mid-complexity agent tasks to Sol, keep your current flagship for the refactors that must land first try, and compare rework rates. GitHub Copilot shops don’t need BYOK at all: Sol landed in Copilot’s model picker for Pro+, Max, Business, and Enterprise on launch day (September 29), per the GitHub changelog — check how it meters against your plan’s premium requests before leaning on it, since Copilot’s June billing change made model multipliers the real price tag.

Hold — accuracy-critical, large-context work: if your sessions live above 272K input or your cost of a wrong edit dwarfs token spend, stay on Claude Fable 5.1 (see our Fable 5.1 backend review) or run the Sonnet 5.5 comparison. Astra, meanwhile, is now very hard to justify as a coding backend: OpenAI’s own eval has Sol matching it at a fifth the price — Astra’s remaining case is the frontier reasoning work that DeepSWE doesn’t measure.

The wider pattern, consistent with our 7-way agent comparison and the cost comparison pillar: frontier-adjacent quality keeps repricing toward $2/$10, and flat-rate subscriptions and $10/$50 flagships both have to re-earn their premium every quarter. Sol is the strongest version of that argument OpenAI has shipped yet.

FAQ

What is the exact model ID for GPT-6.1 Sol? gpt-6.1-sol on the OpenAI API (gateways list it as openai/gpt-6.1-sol). If a tool’s picker doesn’t show it, enter the ID manually through an OpenAI-compatible provider config.

Is GPT-6.1 Sol generally available or in preview? Generally available on the API as of September 29, 2026 — it is live on OpenAI’s platform, Vercel AI Gateway, and GitHub Copilot (Pro+/Max/Business/Enterprise). No waitlist as of October 2, 2026.

Does GPT-6.1 Sol work with Cursor Tab autocomplete? No. Cursor Tab always runs on Cursor’s proprietary completion model. A custom OpenAI key only routes Chat and Agent requests.

How does the 272K long-context surcharge work? Any single request with more than 272,000 input tokens is billed at 2x input ($4/M), 2x cache ($0.20/M), and 1.5x output ($15/M) — for the whole request, not just the tokens past the line. Cap your agent’s context or compact aggressively to stay under it.

Is GPT-6.1 Sol better than Claude Fable 5.1 for coding? Unproven. Sol matches GPT-6 Astra on OpenAI’s own DeepSWE v1.1 (75.22% vs 74.1%), but there is no independent benchmark comparing it to Fable 5.1 yet. At one-fifth the token price, it is worth a week-long trial on your own repo; it is not yet worth migrating accuracy-critical pipelines on vendor numbers alone.

Sources

Last updated October 2, 2026. Pricing and model availability change frequently; verify current state on the official pricing pages before committing to a backend.

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