OpenAI released GPT-5.6 on July 9.
- Sol, Terra, and Luna released.
max— one model reasons for longer.- Sol Ultra — splits large tasks into parallel workstreams and coordinates the results.
- Ultra default — four agents.
- Three days in — praise for complex coding and security review, alongside mounting complaints about rapid usage depletion.
- User playbook — Terra for everyday work, Sol for hard problems. Ultra is effective for large tasks that divide cleanly; smaller tasks carry a heavy usage cost.
OpenAI's results — gains over GPT-5.5
| Evaluation | GPT-5.5 | Sol | Change |
|---|---|---|---|
| Coding Agent Index | 76.4 | 80.0 | +3.6 |
| OSWorld 2.0 | 47.5% | 62.6% | +15.1pp |
| SEC-Bench Pro | 45.8% | 71.2% | +25.4pp |
| HealthBench Professional | 49.5% | 60.5% | +11.0pp |
OpenAI launch data.
Coding — rankings vary by evaluation
| Evaluation | Sol | Claude Fable 5 | Leader |
|---|---|---|---|
| Coding Agent Index | 80.0 | 77.2 | Sol |
| SWE-Bench Pro | 64.6% | 80.0% | Fable 5 |
| DeepSWE v1.1 | 72.7% | 69.7% | Sol |
OpenAI launch table.
Other GPT-5.6 additions
| Feature | Change |
|---|---|
| Programmatic Tool Calling | Runs programs to coordinate tool calls and process intermediate results |
| Explicit prompt caching | Lets developers mark reusable prompt segments |
| Persisted reasoning | Reuses reasoning items from earlier responses in later turns |
| Pro mode | Responses API setting that gives the model more compute before returning one final answer |
| Original image detail | Preserves original image dimensions |
OpenAI API documentation. max is a reasoning effort, Pro mode is a Responses API setting, and Ultra is a parallel multi-agent setting.
Sol Ultra — parallel workstream coordination
| Setting | How it works |
|---|---|
| max | One model spends longer reasoning and checking |
| Sol Ultra | Splits a large task into concurrent workstreams and coordinates the results |
- Default — four agents.
- Tradeoff — higher token usage.
- OpenAI evaluation — higher scores and shorter completion times.
- API — Responses API multi-agent beta.
| Evaluation | Sol | Sol Ultra |
|---|---|---|
| Terminal-Bench 2.1 | 88.8% | 91.9% |
| BrowseComp | 90.4% | 92.2% |
| SEC-Bench Pro | 71.2% | 74.3% |
OpenAI launch data.
Outside evaluation — efficiency and finish quality
| Evaluation | Sol max | Claude Fable 5 max | Result |
|---|---|---|---|
| Artificial Analysis Intelligence Index | 58.9 | 59.9 | Fable 5 +1.0 |
| Coding Agent Index | 80.0 | 77.2 | Sol +2.8 |
| AA-Briefcase rubric score | 42% | 56% | Fable 5 leads |
| Presentation visual quality | 1st | — | Sol leads |
- Artificial Analysis estimate — $1.04 per task for Sol max.
- Roughly one-third the cost of Fable 5 max.
- Rankings change with the evaluation environment.
Post-launch community response — usage overtakes quality as the debate
Public posts through July 12, focused on developers and coding users. These are anecdotes, not a survey or controlled comparison.
Performance — praise on complex work, recurring reports of basic mistakes
- Reddit launch megathread — reports of better intent following, code review, and security-gap detection.
- Large brownfield codebase report — a July 12 account that said the model felt closer to real work after adjusting model and reasoning settings.
- Coding comparison thread — mixed views on frontend gains and general coding quality.
- Counterexamples — users who saw little change from GPT-5.5, or found Sol strong on detail but prone to missing basic constraints.
Usage — Ultra draws the most repeated complaints
- Sol Ultra usage thread — reports that Ultra found issues missed by 5.5, paired with concern over large-scale subagent usage.
- Five-hour allowance reached in under ten minutes — one Pro 5x user's Sol XHigh report. No reproduction or confirmed cause.
- Allowance depleted on a familiar workload — one Pro 20x user's experience, mainly with Sol High. OpenAI has not confirmed an accounting bug.
- GeekNews discussion — separate reports of a simple Ultra task using 20% of a five-hour allowance and an Ultra Fast task using 100% in ten minutes.
OpenAI's documentation says apparently similar tasks can consume different amounts depending on model, context, reasoning, tools, retrieval, and caching. Prompt length alone is not a reliable predictor.
Working setup — Terra for daily work, Sol for hard problems, Ultra selectively
- V2EX model-selection thread — proposals to use Terra for everyday development, Sol for architecture and hard problems, and Luna for repetitive work.
- Three models plus multiple reasoning levels — too many choices, according to some users.
- Requests for automatic model routing based on the task.
- Some Reddit and GeekNews users — stepped down from Ultra to Sol High or XHigh, or moved routine work to Terra.
OpenAI's current recommendation is similar: use the lowest reasoning effort that produces the result you need, and reserve Ultra for large projects that divide into independent workstreams.
Hacker News — prompt migration and benchmark trust
- Launch discussion — interest in leaner prompt guidance and compatibility with existing system prompts.
- Questions over the gap between OpenAI's selected benchmarks and real development performance.
- Concern that shorter default output could change response length in existing services.
Availability
| Product | Audience | Models and settings |
|---|---|---|
| Standard ChatGPT | Plus | Sol Medium and High |
| Standard ChatGPT | Pro, Business, Enterprise | Sol Medium, High, Extra High, and Pro |
| Codex | Free, Go | Terra |
| Codex | Plus, Pro, Business, Enterprise | Sol, Terra, Luna, max, and Sol Ultra |
| Work in ChatGPT | Plus, Business | Sol, Terra, Luna, and max |
| Work in ChatGPT | Pro, Enterprise | Sol, Terra, Luna, max, and Sol Ultra |
| API | API users | Sol, Terra, Luna, and multi-agent beta |
- Standard ChatGPT default — GPT-5.5 Instant remains in place.
- Standard chat model picker — Terra and Luna are not available.
Codex usage — official estimates
| Plan | Sol | Terra | Luna |
|---|---|---|---|
| Plus, Business | 15–90 | 20–110 | 50–280 |
| Pro 5x | 75–450 | 100–550 | 250–1,400 |
| Pro 20x | 300–1,800 | 400–2,200 | 1,000–5,600 |
Estimated local messages per five-hour window. Local messages and cloud tasks share the window; additional weekly limits may apply.
- OpenAI average — 5–40 credits per GPT-5.6 message.
- Actual use — varies with model, context, reasoning, tools, retrieval, and caching.
- Ultra — no separate estimated message range published.
API pricing
| Model | Role | Input | Output |
|---|---|---|---|
| Sol | Flagship | $5 | $30 |
| Terra | Balanced | $2.50 | $15 |
| Luna | Fast, low-cost | $1 | $6 |
Standard API price per 1 million tokens. Separate from ChatGPT and Codex subscription usage.
Still unverified
- Standard ChatGPT conversation and Korean-writing quality.
- Repeated comparisons using the same task and settings.
- Whether rapid Ultra depletion is expected multi-agent usage or a separate accounting issue.
Sources
- OpenAI: GPT-5.6: Frontier intelligence that scales with your ambition
- OpenAI developer documentation: Using GPT-5.6 · API changelog · Pricing (checked July 12)
- OpenAI Help and product documentation: GPT-5.6 in ChatGPT · Models · Codex Pricing (checked July 12)
- Artificial Analysis: GPT-5.6 benchmarks across Intelligence, Speed and Cost
- Hacker News: GPT-5.6 launch discussion
- GeekNews: OpenAI GPT 5.6 launch
- V2EX: Choosing among Sol, Terra, and Luna
- Reddit r/codex: launch megathread · coding comparison · large brownfield codebase report · Ultra usage · Sol usage
