GPT-6.1 Sol Pricing Explained: Starting at $2 Input, How Much Does It Save Versus GPT-5.5?

OpenAI released GPT-6.1 Sol on September 29, 2026, positioning it as an economical option with capabilities approaching GPT-6 Astra, aimed at Plus/Pro/Business/Enterprise/Edu users and available across ChatGPT Work, Codex, and the API. For developers already using GPT-5.5 workflows, the listed pricing difference is quite significant, but several key details are worth unpacking before migration.

Official Pricing

The standard prices and long-context rules below appear in OpenAI's developer documentation (gpt-6.1-sol model page), with third-party coverage from DataCamp and Vellum; the cache write price and limited-time offer dates come from DataCamp.

Context window 1,050,000 tokens; maximum output 128,000 tokens. ⚠ Limited-time note: According to DataCamp, OpenAI's posted current pricing applies at least through November 21, 2026 (this date comes from a single source and has not been independently reported through other channels; please refer to the latest posting on OpenAI's official pricing page).

Cost Gap Versus GPT-5.5

In the YZ Index registry, GPT-5.5's standard pricing is $5.00 input and $20.00 output (per million tokens). The following are arithmetic estimates based on official list prices:

• Scenario 1: 1M input + 200K output — GPT-6.1 Sol totals $4.00 ($2.00 + $2.00), GPT-5.5 totals $9.00 ($5.00 + $4.00); Sol saves about $5, at about 44% of GPT-5.5's cost.
• Scenario 2: 5M input + 1M output (medium batch) — GPT-6.1 Sol totals $20.00, GPT-5.5 totals $45.00; Sol saves $25, a batch-processing cost reduction of about 55%.

Compared with GPT-6 Astra, according to separate reports from DataCamp and Vellum, Astra is priced at $10.00 input, $1.00 cached input, and $50.00 output (per million tokens); GPT-6.1 Sol's input and output prices are each exactly one-fifth of that, consistent with OpenAI's product positioning.

How Caching and Long Context Affect Cost

In short-context scenarios, after a cache hit, the cost is only $0.10 per million tokens, 5% of the original input price. Take "large system prompt + repeated calls" as an example: the first write of an 800K-token cache costs $2.00 ($2.50/million × 0.8M); if subsequent requests all hit the cache, the input cost for the same 800K tokens is only $0.08, down about 95% from $1.60 without caching. This has a substantial impact on agent workflows with long-lived prompts (codebase context, long rule documents, RAG knowledge bases).

The tiering mechanism for long context requires special attention: once a single request's input exceeds 272K tokens, the entire request is billed at the long-context rate, rather than only the portion above the threshold. Input rises from $2.00 to $4.00, and output rises from $10.00 to $15.00. Therefore, if an ultra-long-context task can be split into multiple short-context requests, it may be possible to avoid the premium without losing caching benefits.

Reference Value of the First Test Data

On September 30, 2026, YZ Index completed an 18-question targeted evaluation of GPT-6.1 Sol (Run #348): all 18 questions returned successfully, with no API failures, and a median latency of about 3.9 seconds per question (observed under concurrent evaluation conditions). This only shows that the endpoint is callable and response speed is normal; it does not involve capability assessment. The first run failed due to an API parameter compatibility issue; see this site's integration log article for details.

Boundaries of Migration Advice

Based on official list prices, GPT-6.1 Sol costs about 44% of GPT-5.5 under standard API usage; with prompt caching added, there is room for further reduction. But the following boundaries should be kept in mind:

• Scope differences: This 18-question targeted evaluation's overall score of 95.91 is not the full weekly leaderboard scope and cannot be directly compared for ranking with any model on the main leaderboard; we also used the same 18 questions to run supplementary tests on GPT-6 Astra, GPT-6 Sol, and GPT-6 Luna, but each model was run only 1–2 times, and single-run differences fall within noise (see a follow-up article on this site), which is not enough to determine which is more cost-effective. GPT-6.1 Sol's full baseline will be based on the next weekly Full evaluation.
• Quality assessment remains pending: A lower price does not mean equivalent quality; the current data only reflects endpoint stability and response speed, and a comprehensive capability baseline must wait until the Full run results are available.
• Long-context premium: A single request exceeding 272K tokens is priced higher across the board, so ultra-long-context tasks need separate accounting and cannot simply use standard rates.
• Limited-time pricing risk: According to a single DataCamp source, the current pricing has an expiration date; if it proves accurate, recalculate the actual cost-benefit ratio at that time.

If a team mainly uses standard API calls, keeps prompt lengths manageable, and its existing workflow depends on GPT-5.5, the listed cost savings are already considerable; however, we recommend waiting until this site's full baseline is available and comparing score distributions by actual task type before deciding whether to switch entirely.

References: GPT-6.1 Sol: Features, Benchmarks, Pricing, and Access; GPT-6.1 Sol: Features, Pricing, Context Window & Alternatives; GPT-6.1 Sol Benchmarks Explained: Coding, Computer Use & Pricing; OpenAI releases GPT-6.1 Sol at a fifth of GPT-6 Astra's token prices.