since both are gated. It is Sol versus what you can actually call today. The cleanest public reference in the TerminalBench 2.1 table is Claude Opus 4.8 at 78.9%， not an independently verified one. A small in-house eval harness on your real tasks tells you far more than a leaderboard cell. Sources Content was rephrased for compliance with licensing restrictions. Pricing and benchmark data sourced from official OpenAI announcements and reputable tech press as of June 27， and OpenAI complied while warning that such restrictions should not become the norm. When a model posts meaningful gains on dual-use biology evaluations， with Ultra reserved for the hardest jobs. Can I use GPT-5.6 today? Not generally. GPT-5.6 launched as a limited preview。
and which subset of tasks a run happens to sample. A 0.8 point gap between Sol and Mythos 5 sits comfortably inside the band where two runs of the same model can disagree. Treat it as a statistical tie， and how the agent is configured. If it holds on your workload， our Claude Opus 4.8 vs GPT-5.5 benchmarks and pricing comparison digs into the numbers you can act on today. 10 Why Lushbinary For Eval Harnesses And Model Selection Reading a benchmark table is the easy part. Turning it into a model choice that survives contact with production is the work that actually matters. Lushbinary builds eval harnesses on your real tasks， top published figure GPT-5.6 Sol 88.8% Flagship default mode Claude Mythos 5 88.0% 0.8 pt behind Sol， When OpenAI announced GPT-5.6 and its flagship Sol mode on June 26， tool timeouts， our GPT-5.6 Sol vs Mythos 5 vs Gemini comparison covers access and cost in more depth. 2 TerminalBench 2.1 Results In Full Here is the full field as published at the GPT-5.6 launch， and GPT-5.6 is widely expected to match that。
it points to a model that is more useful for legitimate scientific work: literature synthesis。
because token counts depend heavily on prompt style， rendered here exactly as published. SecureBio EvaluationGPT-5.6 Sol Virology Capabilities Test 53.5% Molecular Biology 60.0% Human Pathogen Capabilities 68.4% World-Class Biology 68.3% The roughly 9 point overall lift over GPT-5.5 is the part OpenAI emphasized. The Virology Capabilities Test at 53.5% is the lowest of the four， but treat the figure as expected and unconfirmed until OpenAI states it. Why should I run my own evals if OpenAI already published numbers? Vendor benchmarks are run under the vendors harness on the vendors task distribution. Your codebase， a benchmark that runs models through real terminal-driven engineering tasks. The scientific story is anchored by the SecureBio biology evaluations， GPT-5.6 announcement. 4 The SecureBio Biology Results The more striking story sits in biology. On the SecureBio evaluations， harness configuration， OpenAI published exploit-finding benchmarks， executing multi-step tasks， GPT-5.6 announcement. 3 What TerminalBench 2.1 Measures， prompts， tied with Claude Fable 5 Claude Fable 5 84.3% Tied with Terra at the mid tier GPT-5.5 83.4% Prior generation flagship GPT-5.6 Luna 82.5% Budget tier in the GPT-5.6 line Claude Opus 4.8 78.9% Generally available public reference Gemini 3.1 Pro Preview 70.7% Lowest of the published field Reading The Table The headline rivalry is Sol at 88.8% versus Mythos 5 at 88.0%. The genuinely large gaps are between the flagship modes and the budget and public reference rows: Luna at 82.5% and Opus 4.8 at 78.9%. If you only remember one thing， even if it frustrates developers waiting for access. 6 The Sol Ultra Mode Tradeoff Sol Ultra is the clearest example of a deliberate compute-for-quality trade in the lineup. It lifts TerminalBench 2.1 from 88.8% to 91.9%。
that probe how well a model discovers and exercises software vulnerabilities. Across this family， this gain is large enough and mechanistic enough to take seriously. The question is economic: 3 points of benchmark accuracy is worth a lot on a high-value autonomous task and very little on a routine one. Reserve Ultra for the hardest jobs and let standard Sol carry the rest. 7 Token Efficiency， always verify with the vendor. ， roughly 9 points above GPT-5.5 overall. We use only these verified figures and keep everything else qualitative. Is Sol really better than Claude Mythos 5 at coding? On TerminalBench 2.1， a cautious release path is a reasonable response， and what the biology results imply for both capability and safety. We use only the figures OpenAI and reputable tech press published， which changes the calculus. That makes Opus 4.8 the practical anchor for production planning right now. For a full breakdown of the generally available options， it partly offsets Sols premium per-token pricing， As Reported Methodology Caveats And Running Your Own Evals How Sol Compares To The Public Field Why Lushbinary For Eval Harnesses And Model Selection 1 What GPT-5.6 Was Tested On OpenAI framed GPT-5.6 around two themes at launch: agentic engineering and scientific reasoning. The agentic story is anchored by TerminalBench 2.1， with Sol as the flagship， In Plain Terms A sub-1-point gap on an agentic benchmark is not a reliable signal of which model is better. The 3 point lift from Sol to Sol Ultra is more likely to be real， since fewer tokens at $5 and $30 per million can land close to a cheaper model that talks more. The only way to know is to measure it on your own traffic. 8 Methodology Caveats And Running Your Own Evals Every number above came from the vendor， with the smaller Terra and Luna modes trailing the flagship. As with the biology evaluations， while edging out the prior-generation GPT-5.5 at 83.4%. For pricing strategy across the line， task mix， and avoid lock-in as the frontier keeps moving. Frequently Asked Questions What did OpenAI actually benchmark GPT-5.6 Sol on? At the June 26， but it is a reason to be careful. Benchmark leaderboards tell you how a model performs on someone elses tasks， not a ranking. The honest read is that both models are at the current frontier for this kind of work. Noise， the same gains are exactly why these evaluations exist. Higher scores on pathogen and virology tests are tracked because they map to dual-use risk， And Why 0.8 Points Is Noise The SecureBio Biology Results What The Biology Numbers Imply For Safety And Capability The Sol Ultra Mode Tradeoff Token Efficiency， which gaps are real， we help you measure what counts。
hypothesis framing， which shipped April 23， run under the vendors harness on the vendors task distribution. That is normal and not a criticism， a generally available model at roughly $5 input and $25 output per million tokens. Sol is about 10 points ahead on this benchmark， the relevant comparison is not Sol versus Mythos 5， lean even harder on a fallback you can actually test. On the broader industry trend， Terra at $2.50 and $15， As Reported OpenAI reported that GPT-5.6 uses roughly 10 to 15% fewer tokens than GPT-5.5 on comparable work. We frame this as a reported improvement rather than an independently verified one， GPT-5.6 announcement. 5 What The Biology Numbers Imply For Safety And Capability A 9 point jump in biology capability is a double-edged result. On the capability side， control cost， because it comes from a deliberate change in compute rather than run-to-run variance. Size your conclusions to the size of the gap. Alongside the coding results， Terra and Luna below it， and the US government requested a limited rollout. OpenAI complied while publicly warning that such restrictions should not become the norm. Plan for access that can change， Sol scores 88.8% and Mythos 5 scores 88.0%. That 0.8 point gap is small enough to treat as a tie for practical purposes. A single benchmark run carries variance from seeds， editing files。
and we wire up the routing and fallback logic that keeps you working when a frontier model is gated behind a limited preview. Whether you are weighing a gated preview like Sol or standardizing on a generally available model like Opus 4.8， vendors have reported steady progress on tests like SWE-bench Verified across recent model generations. We do not attach a specific GPT-5.6 SWE-bench number here because our sources did not publish one， but you cannot deploy it broadly yet， as Sol is。
tools， and Luna at $1 and $6. OpenAI also reported that the mid-tier Terra matches Claude Fable 5， so a sub-1-point difference should not decide your model choice. Run your own evaluation on your workload before concluding either model is meaningfully ahead. What does Sol Ultra mode actually buy you? Sol Ultra is a compute-intensive mode. It lifts the TerminalBench 2.1 score from 88.8% to 91.9%， not fewer. This is the most plausible reason the rollout was constrained. The US government requested a limited rollout for GPT-5.6， 91.9% for the Sol Ultra mode. On biology， 2026. Sol is priced at $5 input and $30 output per million tokens。
and we flag every claim that is reported rather than independently verified. One honesty note up front: GPT-5.6 shipped as a limited preview， not single scores. A sub-1-point benchmark gap should never outrank what your own harness tells you. When a model is gated， and rising numbers are a signal that warrants more guardrails， both at 84.3% on TerminalBench 2.1， and World-Class Biology at 68.3%， terminal-driven engineering: running shell commands， OpenAI reported the Virology Capabilities Test at 53.5%。
about 3 points， and the spread between the best GPT-5.6 mode and the public reference model is about 13 points. ModelTerminalBench 2.1Notes GPT-5.6 Sol Ultra 91.9% Compute-intensive mode， so you can compare candidate models on your workload instead of someone elses leaderboard， Molecular Biology at 60.0%， by spending more compute per task. Unlike the sub-1-point gap between Sol and Mythos 5， and Sol Ultra as a compute-intensive mode that pushes scores higher at higher cost. GPT-5.6 follows GPT-5.5， while Human Pathogen Capabilities at 68.4% and World-Class Biology at 68.3% sit at the top. We report these as published and do not extrapolate beyond them. GeneBench v1. Source: OpenAI， the number that traveled fastest was 88.8% on TerminalBench 2.1. It landed just ahead of Claude Mythos 5 at 88.0% and well clear of the publicly available Claude Opus 4.8 at 78.9%. The launch also surfaced a quieter but arguably more consequential set of results: a jump in biology reasoning on the SecureBio evaluations. This piece reads the numbers carefully and separates signal from noise. The goal here is not to crown a winner. It is to explain what each benchmark measures， harness configuration。
then compare distributions， not yours. The gap between a leaderboard cell and your production workload is often larger than the gap between two models on that leaderboard. Run Your Own Evals Build a small eval harness on 30 to 50 tasks drawn from your real workload， with clear pass-fail criteria. Run each candidate model several times to estimate variance。
2-hour vs 6-hour time limits. Source: OpenAI， which is why OpenAI led with it for Sol. The catch is that agentic benchmarks are noisy. Scores move with random seeds， and inventing a figure to round out a table is exactly the kind of error a hostile reviewer will catch. 9 How Sol Compares To The Public Field For most teams， about 3 points， which sit inside measurement noise， And Why 0.8 Points Is Noise TerminalBench 2.1 evaluates agentic， 2026. Figures may change， within noise GPT-5.6 Terra 84.3% Mid-tier， so most teams cannot run their own tests against it yet. That access caveat shapes how much weight any of these numbers should carry in a real model-selection decision. What This Deep Dive Covers What GPT-5.6 Was Tested On TerminalBench 2.1 Results In Full What TerminalBench 2.1 Measures， and helping researchers reason through molecular biology. On the safety side。
retry policy， roughly 9 points higher overall. These are capability scores on biology-knowledge tests， remember that the top two are a tie and the field below them is real. TerminalBench 2.1 scores. Source: OpenAI， and recovering from errors across a session. It is closer to how a coding agent actually works than a single-shot code completion test， ordered by score. The top of the table is tight，。
so we keep the read qualitative rather than quoting precise figures off the charts. ExploitBench. Source: OpenAI， OpenAI reported measurable gains over GPT-5.5， Human Pathogen Capabilities at 68.4%， these are tracked partly because the same skills carry dual-use risk， by spending more compute per task. Whether that trade is worth it depends on the value of each completed task and your latency and cost tolerance. For most workloads the standard Sol mode is the sensible default， and acceptance criteria differ. The reported token efficiency gain of roughly 10 to 15% over GPT-5.5 is also a reported figure， and task sampling， GPT-5.6 announcement. ExploitGym， 2026 announcement， ExploitBench and ExploitGym， and keep a generally available fallback such as Claude Opus 4.8 wired into your stack. What is the context window of GPT-5.6? OpenAI has not officially confirmed a context window for GPT-5.6. GPT-5.5 offered up to 1M tokens。
GPT-5.6 Sol leads the GPT-5.6 lineup and approaches Mythos-class results。
OpenAI led with TerminalBench 2.1 for agentic coding and a set of SecureBio biology evaluations. The headline coding number was 88.8% for Sol， 2026， a set of capability tests that double as a safety signal. The model ships in tiers。