GPT-6 Sol vs GPT-5.6 Sol: Half the List Price, Same 1.05M Context — Should You Upgrade?
What the official references say
| Item | GPT-5.6 Sol | GPT-6 Sol |
|---|---|---|
| Model ID | gpt-5.6-sol; the gpt-5.6 alias points here | gpt-6-sol (snapshot ID gpt-6-sol) |
| Positioning | Complex professional work | Complex coding and agentic workflows |
| Input / output | Text and image / text | Text and image / text |
| Context / maximum output | 1,050,000 / 128,000 tokens | 1,050,000 (922,000 max input) / 128,000 tokens |
| Knowledge cutoff | Earlier 5.6 cutoff | April 20, 2026 |
| Reasoning effort | none, low, medium (default), high, xhigh, max | Same six levels, medium default |
| Endpoints | Responses, Chat Completions | Responses, Chat Completions |
| Function calling | Listed in the model reference | Responses at any effort; Chat Completions only with reasoning_effort: none |
| Streaming, structured outputs | Listed | Listed |
| Official list price | Standard, long-context (above 272K input), Batch, Flex, Fast rows; a promotional Standard rate runs through November 21, 2026 | Half the GPT-5.6 Sol promotional Standard rate, per OpenAI; same tier structure |
| OpenAI-reported coding result (vendor) | Baseline for OpenAI's comparisons | DeepSWE v1.1 68.8% at max; Terminal-Bench and coding-agent index gains reported by Artificial Analysis |
| OpenAI-reported deception rate (vendor) | 10.4% in OpenAI's internal coding-deception test | 1.3% in the same test |
| EvoLink route | Callable; see the GPT-5.6 page for the current rate | Callable since September 23, 2026; see the GPT-6 Sol page for the current rate |
Three things are the same size and easy to mistake for "the same": context window, maximum output and the six reasoning levels. Four things are different and matter: the halved list price, the knowledge cutoff, the tool-calling rule on Chat Completions, and OpenAI's own reported gains in coding results and agent honesty. The first three are contract facts you can plan on today; the fourth is a claim to test.
The contract change that breaks agents: tools on Chat Completions
reasoning_effort is none. On the Responses API, function calling works at any effort. A GPT-5.6 Sol agent that calls tools through Chat Completions with medium reasoning has three options on GPT-6 Sol:- Move the agent to the Responses API and keep reasoning enabled.
- Stay on Chat Completions and set
reasoning_effort: none, accepting a different quality profile. - Split: reasoning steps on one call, tool execution on another.
Decide this before the quality trial, because it changes the comparison. Option 2 compares GPT-6 Sol without reasoning against GPT-5.6 Sol with it; option 1 changes the endpoint and possibly the streaming and event contract your client parses. Record which option the trial uses and do not average results across options.
Freeze a useful GPT-5.6 Sol baseline
Select recent tasks with known acceptance criteria rather than a set assembled to flatter a model. Include short edits, multi-file changes, a tool failure and a job that previously needed human rescue. Keep secrets and private customer data out of a reusable evaluation bundle.
gpt-5.6 alias, resolve and record that it meant Sol at the time of the run.Run your existing route using normal production settings. A higher reasoning effort is not a free improvement: it can change output length, time and spend. Use a common supported setting for the first paired comparison, then report any tuned configuration separately.
A minimal run record should contain:
| Field | Why it matters |
|---|---|
| Task ID and repository commit | Prevent comparing different code or requirements |
| Provider, requested and returned model identity | Make route changes and alias resolution visible |
| Endpoint, prompt/harness revision and controls | Distinguish model changes from endpoint or instruction changes |
| Test result and reviewer verdict | Separate executable correctness from presentation |
| Tool calls, failures and interventions | Expose work moved from the model to people |
| End-to-end time and total billed usage | Include retry and repair costs |
Retain failed runs. Removing timeouts or only averaging successful attempts can make a fragile candidate look unusually efficient.
Six tasks that expose GPT-6 Sol migration risk
The following examples define what to check. Adapt them to your application; they are not claims that either model will pass.
| Task | Evaluation input | Acceptance and failure signal |
|---|---|---|
| Repair a reproducible bug | Issue, fixed repository revision and failing test | Original failure is repaired, regression suite passes, and unrelated behavior is unchanged |
| Review a patch | Diff plus the surrounding functions | Findings identify a reproducible defect and its location; unsupported warnings count against precision |
| Diagnose a failed build | Build logs and a reproducible environment | Proposed cause can be reproduced and the repair passes the same build, without bypassing tests |
| Change an API contract | Typed request/response schema and existing callers | Updated callers compile; invalid input and error responses preserve required behavior |
| Recover from a tool failure | A deliberately failed read or interrupted command | Agent reports uncertainty or retries within policy; it does not fabricate a successful tool result |
| Complete a multi-file feature | Written requirements with functional and UI checks | All acceptance criteria pass, reviewer intervention is recorded, and external writes require the expected approval |
Choose the judging rule before running either model. For patch tasks, passing tests is necessary but may not cover the full requirement. Have a reviewer inspect the diff without seeing the model name where practical. Record both the initial submission and the final result after allowed repair attempts.
Repeated runs help expose variability. Start with a manageable pilot to discover harness problems, then expand the sample around expensive failures. Do not declare a reliable percentage-point improvement from a few tasks; report the sample size, task mix and the number of paired disagreements.
Check the request contract before the quality trial
Run a compatibility pass on the exact provider route before spending on a larger trial. Both EvoLink and the OpenAI API can serve this pass today.
| Contract check | Passing evidence |
|---|---|
| Model identity | Request gpt-6-sol, response identity matches; no alias substitution |
| Endpoint and authentication | Your client completes a request on the endpoint you chose above and handles documented errors |
| Reasoning and output controls | Required effort level is accepted; on Chat Completions with tools, only none is documented |
| Tool calling | Arguments parse, tool results reconnect to the correct call, and errors remain visible |
| Streaming | Partial events assemble correctly; cancellation and interrupted streams do not produce false success |
| Structured output | Required schema passes, while refusal, truncation and invalid output follow explicit handling |
| Context and usage | Your real input fits 922,000 input tokens; usage categories reconcile with billing, including the long-context tier above 272K |
Do not copy Astra's tool rule (Responses only) or its price rows into a Sol configuration. A unified gateway reduces integration work, but each model still needs a verified capability contract. Keep the old route selectable through configuration so a rollout does not require rewriting prompts throughout the application.
Compare cost per accepted task, not list price
The official pricing page shows GPT-6 Sol's Standard rate below GPT-5.6 Sol's current promotional rate. That is a fact about tokens. Your budget is about accepted work:
API cost per accepted task = total billed evaluation spend / number of accepted tasksThe numerator includes failed attempts, retries, repair calls and any escalation model. Report reviewer minutes beside that number. If no task passes, the ratio is undefined and the candidate has failed this acceptance set.
Set the upgrade and rollback gates in advance
Use requirements your team can defend, rather than a universal "10% better" threshold. A security-sensitive tool violation may be a stop condition even when aggregate patch success rises. A slower response can be acceptable for an offline job and unacceptable for an interactive assistant.
| Decision | Conditions to record |
|---|---|
| Keep the existing route | Required tool-with-reasoning behavior is unavailable on your endpoint, identity is uncertain, critical regression occurs, or your latency/cost requirement is missed |
| Run a limited candidate pilot | Contract passes, representative tasks meet acceptance, and uncertainty is small enough for the proposed exposure |
| Expand gradually | Production observations match the trial within your chosen error, latency and cost budgets |
| Roll back | Error rate, review burden or spend crosses the stop condition set before rollout |
A shadow trial should avoid external side effects: simulate writes or use an isolated environment. In a live pilot, a timeout after a tool write does not prove the write failed. Check state before replaying on a fallback model.
Where does GPT-6 Luna fit into this decision?
FAQ
Is GPT-6 Sol better than GPT-5.6 Sol?
OpenAI positions it for complex coding and agentic workflows with a newer knowledge cutoff. This guide has no measured comparison on your tasks; compare accepted work under a recorded, matched evaluation.
What is the biggest API difference between the two?
reasoning_effort: none. Agents that use tools with reasoning should plan on the Responses API.Is GPT-6 Sol cheaper than GPT-5.6 Sol?
Should I stop shipping on GPT-5.6 Sol now that GPT-6 Sol is on EvoLink?
No. Preserve the baseline, prepare evaluation tasks, decide the endpoint and run the contract pass. GPT-5.6 Sol keeps its own documented route.
Can I keep the same model parameters?
The six reasoning levels, context and maximum output are the same. The tool-calling rule on Chat Completions is not. Check endpoint, tools, streaming and output schema on the exact route before a larger quality trial.
Are the dollar examples benchmark results?
No. They are hypothetical arithmetic illustrating the denominator. They are not prices or measured performance of either Sol generation.
