Choosing between GPT-6.1 Sol and Claude Opus 5.5? Start with the tasks you need to complete and the cost of a full request. Sol has lower official short-context token rates; Opus 5.5 keeps standard rates across its 1M context window. Their published evaluations use different setups, so neither has a universal win. The official rate tables appear below.
Specifications and positioning
| Dimension | GPT-6.1 Sol | Claude Opus 5.5 |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Native API ID | gpt-6.1-sol | claude-opus-5-5 |
| Context window | 1,050,000 tokens | 1M tokens |
| Standard maximum output | 128,000 tokens | 128K tokens |
| Input → output | Text and images → text | Text and images → text |
| Default reasoning effort | medium | medium |
| Reasoning parameter | reasoning.effort | output_config.effort |
| Native tool-calling interface | Responses API | Messages API |
Sources: OpenAI model page, Opus 5.5 overview. Sol separately specifies a maximum input of 922,000 tokens. Opus supports 300K output through Message Batches under a specific beta configuration; that is not its standard synchronous limit.
A larger advertised window does not translate directly into proportionally more usable documents. Tokenization and context management differ. Count tokens on your own files, reserve output capacity and test retrieval accuracy.
Coding and agents: published test results
OpenAI and Anthropic have published several coding and agent evaluations. The table pairs each result with key test conditions, so you can judge which findings are relevant to your work.
| Test | Published result | Where it applies |
|---|---|---|
| OpenAI: AutomationBench | Sol at medium: 2.2 points above Opus 5.5, about one-third the task cost | Result for this workflow and test setup |
| OpenAI: GDP.pdf | Sol above Opus 5.5 with fallbacks, at under half the task cost | PDF tasks and fallback conditions matter |
| Anthropic: Terminal-Bench 4.0 | Opus 5.5: 66.4% at xhigh; comparison includes Astra and GPT-5.6 Sol | GPT-6.1 Sol was not tested in this table |
The test results come from OpenAI's Sol launch evaluation and Anthropic's Opus 5.5 announcement. OpenAI's competitor figures come from public reports. Anthropic used different reasoning levels and, for some safeguarded tasks, continued with other Claude models. The test conditions differ, so read each result on its own terms rather than combining them into one overall ranking.
For software work, score test success, behavioral regressions, repeated prompting and review effort separately. A convincing-looking patch can still be expensive to validate or repair.
Documents, writing and factual reliability
The PDF evaluation gives Sol one documented reference point. Anthropic emphasizes long-running work and clearer communication, but its testimonials and preferences do not establish that Opus is superior for every writing brief.
Use two acceptance layers. For factual quality, check citations, calculations, omissions and acknowledgement of uncertainty. For expression, assess organization, tone, terminology and editability. Giving both models the same conflicting sources can be more informative than asking each to “write professionally.”
There is little public testing of these two models under the same conditions for Chinese writing, translation or specialist work. If those tasks matter to you, run a blind review with your own materials. For recent facts, give both models the same up-to-date sources and check their answers and citations.
Vision and computer use
Both accept text and images and return text. Sol lists computer use among its tools. Opus migration guidance requires integration changes, including rejection of the older computer_20251124 tool on the Claude API and Google Cloud.
Read OSWorld scores alongside the test version. OpenAI reports 2.0 offline partial reward, release v2026.08.08; Anthropic labels its result 2.1 partial. The different versions and scoring methods do not support a reliable lead calculated from these numbers.
Instead, run the same browser workflow and track incorrect actions, recovery, final state and human intervention. Keep native modalities separate from platform tools: access to an image-generation tool does not make the reasoning model a native image-output model.
Official API prices: split short and long context
All figures below are Standard rates in US dollars per million tokens. They are not subscription prices or PoYo quotes.
| Charge | Sol: input ≤272K | Sol: input >272K | Opus 5.5 |
|---|---|---|---|
| Uncached input | 2.00 | 4.00 | 4.00 |
| Cache read | 0.10 | 0.20 | 0.20 |
| Cache write | 2.50 | 5.00 | 5.00 (5m) / 8.00 (1h) |
| Output | 10.00 | 15.00 | 20.00 |
Sources: OpenAI API pricing, Anthropic API pricing. Sol's long-context rates apply to the entire request once the threshold is crossed. Opus retains standard rates across 1M. Cache lifetime and behavior differ, so matching write prices would not establish feature equivalence.
Two example request costs
These estimates use the same billable token counts for both models, Standard processing, and no cache, regional premium or tool fees. Actual tasks may use different numbers of tokens.
| Assumed usage | GPT-6.1 Sol | Claude Opus 5.5 |
|---|---|---|
| 100K input + 10K billable output | 0.1×2 + 0.01×10 = $0.30 | 0.1×4 + 0.01×20 = $0.60 |
| 300K input + 10K billable output | 0.3×4 + 0.01×15 = $1.35 | 0.3×4 + 0.01×20 = $1.40 |
Sol costs 50% less in the first example and about 3.6% less in the second. Actual spend also depends on reasoning tokens, cache hits, retries and successful completion.
A useful purchasing metric is total invocation spend divided by accepted tasks. Lower token prices can be outweighed by extra attempts or expensive human corrections.
Reasoning controls and migration effort
Both offer low, medium, high, xhigh and max, but equal labels do not establish equal compute budgets. Sol rejects none and minimal; Opus 5.5 keeps adaptive thinking on.
Sol tool calls use Responses; Chat Completions requests cannot include tools. Opus rejects forced tool_choice values of any or a named tool, and applications need to handle preserved thinking blocks and their display. Swapping a model string is not enough to guarantee a working agent loop.
Check request parameters, tool-result handling, structured-output validation, refusals and timeouts. Then test an entire multi-turn task. With a routing provider, validate the capabilities that its actual endpoint exposes rather than assuming complete native feature parity.
Speed, access and deployment
Both providers offer processing modes, but their speed claims usually compare a model with its own baseline. To compare Sol and Opus directly, test the same tasks in the same region and include queueing, reasoning, tool execution and rework in the elapsed time.
Opus documentation lists the Claude API, Amazon Bedrock, Google Cloud and Microsoft Foundry among its access routes. Sol documents US and EU data residency, with Fast unavailable for EU residency.
Review the actual contract, region, retention policy and endpoint features. Provider safety evaluations use different systems and are not interchangeable compliance certifications. Application permissions remain a separate engineering responsibility.
Evaluating through PoYo
See the PoYo pages for Claude Opus 5.5 and GPT-6.1 Sol. As of September 30, 2026, the Sol page says “coming soon.” Before testing through PoYo, check each page for access, pricing, caching, long-context terms and supported tools; these may differ from the native APIs.
For other options, browse PoYo's OpenAI provider page and the AI Chat API collection.
Choosing a starting point
Use your priorities to decide which model to try first:
| Priority | Suggested evaluation |
|---|---|
| Official short-context token cost | Start with Sol's lower input and output rates |
| Frequent very long inputs | Test both; Sol's surcharge narrows the rate advantage |
| Existing Claude tools or cloud deployment | Keep Opus as a baseline and account for migration cost |
| Existing Responses integration | Keep Sol as a baseline and validate the tool loop |
| Chinese, specialist writing or internal research | Run blind reviews on representative materials |
| Low-latency interaction | Measure first useful output, full completion and tail latency |
You can start with a small pilot of 20–50 real tasks, fixed inputs, tools and budgets, and several runs per model. Keep failure cases. Before a wider rollout, expand the test and compare success rates, bills and review effort.
For more context, read our GPT-6.1 Sol features guide and OpenAI DevDay 2026 recap.