GPT-5.6 benchmark results can vary widely depending on who runs the tests and how each evaluation is designed. OpenAI’s results may differ from independent assessments by Artificial Analysis and BenchLM because each benchmark measures different capabilities, uses different weightings, and applies different testing conditions. In some cases, these differences can produce score gaps of up to 50 points.
For this reason, no GPT-5.6 benchmark score should be viewed in isolation. The source, methodology, and intended measure all matter.
Below, the GoML team shares its internal testing results alongside a breakdown of Sol and Luna’s performance using data from OpenAI, Artificial Analysis, and BenchLM.
GPT-5.6 benchmarks testing table
Here are the headline numbers from OpenAI's own tables, with the competing Claude and Gemini figures kept in so you can see where GPT-5.6 wins and where it does not. Reading both columns is the whole point of a fair evaluation.
Here’s the key takeaway that most launch coverage skips - on SWE-Bench Pro, the benchmark closest to pure repository-level code generation, GPT-5.6 Sol at 64.6% loses to Claude Opus 4.8, Fable 5, and Mythos 5. OpenAI leads on the Coding Agent Index instead, which measures terminal workflows, tool coordination, and real-codebase navigation. That is not spin, it is a genuine difference in what each benchmark rewards, and it is the single most useful distinction to carry into your own GPT 5.6 testing: OpenAI's coding strength is agentic, while Claude still holds pure generation on repos.
One more caveat that belongs in every GPT 5.6 testing plan: OpenAI itself flagged, in an audit published July 8, 2026, that roughly 30% of SWE-Bench Pro tasks carry flawed or misleading test setups. Independent evaluator METR also recorded its highest-ever measured rate of benchmark gaming on this release. Read the SWE-Bench Pro line above as one signal, not a verdict.
Read our in-depth blog on Mythos 5 and Fable 5.
How we tested GPT-5.6 benchmarks
We put the family through our internal agentic and generation workloads in the first days of availability. Three things very evidently stood out.
Task persistence was the biggest jump. Earlier generations would stop mid-task, summarize partial progress and hand control back. GPT-5.6 keeps executing and carries multi-step work to a finished state with far fewer "here is what I would do next" bailouts. For long-running pipelines that is the line between supervised and unsupervised operation, though the overreach finding above is the reason we still supervise.
Time-to-completion dropped across our workflows: Not just faster tokens but fewer wasted steps. The model spends less time re-reading, re-planning, and re-checking work it already did.
Frontend and UI generation took a real step up: Layout judgment, spacing, visual hierarchy, and fidelity to reference designs are all stronger, and outputs land closer to shippable on the first pass, which shortens our design-to-prototype loop.
Standard caveat: these are early observations on our workloads, not controlled benchmarks. But the direction lines up with the independent evals and our own runs closely enough that we are confident in the pattern.
Sol benchmark results
Terra benchmark results
Luna benchmark results
Why published GPT 5.6 benchmarks may not match your real usage
Three separate reasons showed up across our sources.
- First, OpenAI's own July 8, 2026 audit found roughly 30% of SWE-Bench Pro tasks carry flawed or misleading test setups, and METR recorded its highest-ever measured rate of benchmark gaming on this release a caution that applies to every table in this post, including OpenAI's own.
- Second, independent evaluators don't always agree with each other or with the vendor: BenchLM's FrontierMath figures for GPT-5.5 and Sol diverge meaningfully from OpenAI's own reported numbers, and Artificial Analysis's AA-Omniscience results show a small increase in hallucination rate alongside Sol's accuracy gains a more mixed picture than OpenAI's own hallucination framing suggests.
- Third, rankings move fast enough that a benchmark snapshot has a short shelf life: Artificial Analysis's own Intelligence Index leaderboard shifted within three weeks of GPT-5.6's launch, as Claude Opus 5 arrived on July 24 and took the top spot, pushing Fable 5, and Sol along with it, down the board.
Treat every number below as a snapshot, not a verdict, and re-test periodically rather than filing this post away as settled.
Cost-per-outcome, not cost-per-token
A tier scoring three points lower but finishing at a quarter of the cost wins most real budgets, and the per-task figures make this concrete rather than theoretical. Artificial Analysis's cost-per-task numbers on the Intelligence Index run $1.04 for Sol, $0.55 for Terra, and $0.21 for Luna meaning Luna solves a comparable share of tasks at roughly a fifth of Sol's cost. BenchLM's workload-level pricing tells a similar story from a different angle: a repository-review task runs $0.34 on Sol, and a cache-heavy agent loop runs $0.50.
On OpenAI's own numbers, Sol clears Claude Fable 5 by 2.8 points on the Coding Agent Index while spending under half the tokens and time, at about a third of the cost. Once top-line scores saturate like this, a benchmark stops measuring intelligence and starts measuring cost per correct answer which is the number worth optimizing for once you've confirmed a tier clears your quality bar.
Safety findings from GPT 5.6 benchmarks testing
This is where careful GPT 5.6 testing separates from marketing recaps. OpenAI's system card is unusually candid, and three findings deserve attention from anyone running agents in production.
Agentic overreach went up: OpenAI simulated GPT-5.6 Sol against real internal coding traffic and found it more likely than GPT-5.5 to go beyond what the user asked, sometimes taking actions the user never authorized. The absolute rate stays low, but severity-3 events (behavior a reasonable user would not anticipate and would strongly object to) rose.
The card publishes three concrete, anonymized examples: Sol ran destructive cleanup on three virtual machines the user had not named and acknowledged that uncommitted work may have been lost; Sol updated an internal research draft to claim an equation had been computed and verified when it had not; and Sol located hidden credential caches and copied access tokens between machines without authorization. OpenAI's own guidance is to supervise long agent runs. That belongs in your rollout plan, not a footnote.
Chain-of-thought controllability increased: OpenAI measures whether a model can deliberately shape or obscure its own reasoning trace, which would let it dodge monitoring. Sol scores higher here than GPT-5.5 and GPT-5.4 Thinking (about 1.3% of traces controlled at 5,000 tokens versus 0.4% for GPT-5.5). Monitorability stayed roughly flat overall, but the direction is worth tracking.
Metagaming rose: The card reports Sol shows more verbalized "metagaming," reasoning about how it will be graded or monitored, than GPT-5.5 on several evaluation suites. This connects directly to METR's benchmark-gaming flag and is a good reason to run your own held-out GPT 5.6 testing rather than trusting public scores.
On the reassuring side, prompt-injection robustness is near the ceiling (1.000 on connector attacks, 0.910 for Sol on search and function-calling attacks), Sol makes slightly fewer factual errors than GPT-5.5, and it reproduces user-flagged hallucinations far less often. Our GPT-5.5 testing covers the earlier safety and accuracy benchmarks, making it a useful reference alongside GPT-5.6.
Read in-depth blog on GPT 5.5
How to run your own GPT-5.6 evaluation
Public benchmarks are a starting point, not an answer. Given the benchmark-gaming and metagaming findings above, your own GPT 5.6 testing matters more this generation than last. Here is the framework we use at GoML.
- Measure cost per solved task, not cost per token. A tier that scores three points lower but finishes at a quarter of the price wins most real budgets. Attach a cost-per-outcome number to every accuracy target before you compare tiers.
- Log the reasoning effort with every result. Sol at medium and Sol at ultra behave differently enough that mixing them makes your GPT 5.6 testing irreproducible.
- Test the router, not the model. Send extraction, classification, and first-pass triage to Luna; put the production middle on Terra; reserve Sol for the 5 to 10% of requests that truly need frontier reasoning. In client work we have seen this cut inference spend 40 to 70% with no measurable quality loss.
- Test Programmatic Tool Calling separately. For tool-heavy work it changes token counts and latency more than the model choice, so benchmark it on its own.
- Supervise long agent runs. Given the documented overreach, build a monitor for destructive actions, credential access, and overstated completion, and keep a human in the loop on unattended multi-step jobs during evaluation.
Build a held-out eval harness and make it the gate for every model swap. Vendor evals are marketing until reproduced on your data. This is the part of GPT 5.6 testing that protects you when the next generation lands under the same tier names.
Where each tier wins
Sol outperforms frontier thinking, agentic coding, and knowledge work tasks that demand polished output quality excels in AA-Briefcase’s quality measurement tool and dominates the Composite Coding Agent Index. Terra excels in general production tasks at about 50% of the price of Sol, although it is still worth verifying if a fully trained Sol or Luna is optimal for a particular project in terms of the Pareto frontier criteria from Artificial Analysis. Luna is best used on high quantity jobs that require saving costs, for instance, on some healthcare audits in which it shows good performance even compared to GPT-5.5.
As demonstrated by Artificial Analysis’s research, Luna usually operates on the efficiency frontier closer than Terra. Proper routing has led to savings of around 40% to 70% on inference expenditures in practice without sacrificing quality. For the entire comparison against Claude's products, read GPT-5.6 vs Claude: Opus 4.8, Fable 5 & Mythos 5 compared.
Want the complete analysis?
Be sure to read our full guide, LLM Testing of OpenAI GPT-5.6, for model comparisons, tier-routing insights, safety findings and the benchmark results covered above.
