Is GPT-5.5 or 5.6 Sol better?

The Upgrade Dilemma: Better Benchmarks, Worse Real-World Results

Here's the paradox that should make any team pause before clicking "upgrade": GPT-5.6 Sol beats GPT-5.5 on nearly every public benchmark, costs roughly 3x less per task, and still scored lower on real enterprise agent work.

The public numbers look decisive. Sol wins 6 of 7 shared benchmarks per a Requesty comparison, and OpenAI reports state-of-the-art results across coding, cybersecurity, and knowledge work with fewer tokens. Artificial Analysis pegs cost per task at $0.50 versus GPT-5.5's $1.54, with first tokens arriving in 3.43 seconds instead of 12.64.

Then Toloka ran the test that matters. In a rigorous paired evaluation of 711 frozen enterprise workflow tasks — five runs each, statistically corrected — every effort-matched GPT-5.6 configuration scored lower than GPT-5.5. Sol medium dropped 3.7 points; xhigh dropped 4.0. For context, the GPT-5.4-to-5.5 upgrade gained 3.0 points on the same tasks, and all 11 other within-family upgrades moved upward. GPT-5.6 is the exception.

The failure mode has a name: "surplus action." Sol does the intelligent part of a task, then keeps acting — making 4.8 to 8.5 points more exact duplicate calls and 7.8 to 12.0 points more read-write continuations than GPT-5.5. In stateful enterprise systems, that means duplicate records and extra written state. The model got smarter and simultaneously got worse at knowing when to stop.

Why leaderboards can't answer "should I upgrade?" comes down to three gaps:

• Public benchmarks are frozen, single-shot puzzles; enterprise workflows are long, stateful sequences where a model's stopping behavior matters more than its peak intelligence.
• The signals genuinely conflict — Requesty shows GPT-5.5 winning the agentic τ²-Bench 93.9% to 85.1%, while OpenAI claims new SOTA on Terminal-Bench. Agentic performance is contested territory, not settled science.
• Benchmarks measure capability, not permission. As Elser.ai puts it, "capability and permission are separate" — a stronger model with broad write access can simply do more damage.

The practical takeaway for agent builders: don't upgrade production agents on leaderboard evidence. Re-run your own workflows in a paired comparison and gate success on terminal-state checks — verifying what actually got written — rather than the model's confident read-backs. And because model upgrades change how agents use their attached tools, it's worth validating those skills independently too; that's exactly the problem SkillzBench is building for with its free skills directory, security scanning, and sandboxed Lift benchmarks.

Sol may well be the better model for your daily work. Whether it's better for your agents is a question only your own tasks can answer.

Where Sol Wins: Speed, Cost, and Daily Knowledge Work

For most people doing real work every day — writing, coding, research, analysis — the case for GPT-5.6 Sol is unusually one-sided. It wins on benchmarks, it wins on cost, and it wins on how it feels to use. The question is no longer whether Sol is better; it's whether your workload sits in the narrow band where GPT-5.5 still holds the edge.

According to Requesty's head-to-head comparison, Sol beats GPT-5.5 on 6 of 7 shared benchmarks, including a higher Intelligence Index (47.0% vs 38.4%) and Coding Index (77.4% vs 74.9%). The efficiency gains are just as striking. Independent testing by Artificial Analysis found Sol roughly 3x cheaper per task ($0.50 vs $1.54), with a time to first token of 3.43 seconds versus 12.64 — nearly 4x faster to start responding.

Sol is also dramatically more token-efficient. At medium reasoning effort, it produces about 8k output tokens per task versus GPT-5.5's 16k, and 3k reasoning tokens versus 9k. That efficiency compounds: OpenAI reports internal researchers using more than 2x the daily output tokens observed at GPT-5.5's peak.

The numbers match lived experience. Testers at Every.to called Sol a "serious step change" for daily knowledge work. CEO Dan Shipper said returning to other models felt like "going back to the Stone Age," while Mike Taylor called it "the best and most cost-effective all round model that I would use every day." Andrey Galko noted Sol "does less random stuff, needs fewer follow-ups, and carries more work end to end than GPT-5.5."

GPT-5.6 also introduces tiered choice. Per OpenAI's preview, the family spans Sol (flagship), Terra (balanced, matching GPT-5.5 at 2x lower cost), and Luna (lowest cost). Elser.ai calls Terra "the most sensible first challenger for general coding," reserving Sol for hard, high-value tasks.

The older model isn't obsolete everywhere:

• Long-context reasoning: GPT-5.5 scores 84% vs Sol's 80% on AA-LCR.
• Humanity's Last Exam: 45% vs Sol's 42% — raw frontier reasoning still favors GPT-5.5.
• Output speed: 102.1 vs 88.6 tokens per second — GPT-5.5's only win on efficiency metrics.

For agent builders, the takeaway is clear: Sol is the better default for knowledge work, but the skills and tools you attach to any model matter just as much. SkillzBench is building a free directory and sandboxed benchmarks to verify exactly that — because a stronger model with a risky skill is still a risky agent.

Where GPT-5.5 Still Wins: Production Agents and the 'Surplus Action' Trap

A smarter model that scores worse on your actual workflows sounds impossible — yet that's exactly what Toloka documented when they ran GPT-5.6 Sol against GPT-5.5 on real enterprise tasks. On 711 identical private workflow tasks, every effort-matched GPT-5.6 configuration scored lower than GPT-5.5. Sol at medium reasoning dropped 3.7 points (65.7% down to 62.0% absolute success), and Sol at extra-high dropped 4.0 points — both statistically significant at p ≤ 0.0016. For context, the GPT-5.4-to-5.5 upgrade was worth +3.0 points on the same tasks, and 11 of 11 prior within-family upgrades moved up. GPT-5.6 is the exception.

The failure mode is "surplus action." Toloka's behavioral analysis found Sol doesn't fail at the hard part — it "does the intelligent part, then keeps acting." Across five runs per task, Sol produced 4.8 to 8.5 points more exact duplicate tool calls and 7.8 to 12.0 points more read-write continuation than GPT-5.5. In a stateful workflow, that means duplicate records, extra writes, and corrupted state — errors that never show up on a public leaderboard.

Why stateful, tool-using agents expose this weakness:

• Stateful workflows punish extra actions — a duplicate write can't be undone by a cleverer next step.
• Sol's read-backs look like verification but aren't: it performed 3.2–5.8 points more read-back after final writes and still failed more often.
• The independent τ²-Bench agentic lane shows the same signal — GPT-5.5 wins 93.9% vs Sol's 85.1%.

Toloka's practical advice is blunt: don't upgrade production agents on leaderboard evidence; re-run your own workflows paired, and gate success on terminal-state checks rather than read-backs. Elser's analysts add a principle worth pinning above every agent console: capability and permission are separate. A stronger model doesn't earn broader write access — it may need tighter tool scoping, since more capability can mean more surplus actions.

That separation is exactly the design philosophy behind SkillzBench's approach to equipping agents: per-agent loadouts where the agent only sees the tools and skills you've deliberately chosen for it, with security scanning and sandboxed benchmarking to verify what actually helps before it touches production. If your agent is doing the intelligent part and then keeps acting, the fix isn't always the model — sometimes it's what you let it see.

How to Decide: A Practical Upgrade Protocol for Your Workflows

Benchmarks tell you which model is smarter; your own workflows tell you which one is safer to deploy. The GPT-5.5 versus GPT-5.6 Sol decision splits cleanly along that line, and following a disciplined protocol keeps you from upgrading into a regression.

For knowledge work, coding, and research, switch to Sol now. It wins 6 of 7 shared benchmarks per Requesty's comparison, runs roughly 3x cheaper per task at matched reasoning levels per Artificial Analysis, and practitioners describe it as a serious step change over GPT-5.5. For production agents, hold.

The reason is Toloka's paired evaluation: on 711 identical private enterprise tasks, every effort-matched Sol configuration scored lower than GPT-5.5 — Sol medium dropped 3.7 points. The failure mode, "surplus action," means the model does the intelligent part, then keeps acting, creating duplicate records and writing extra state. Toloka's guidance is blunt: never upgrade production agents on leaderboard evidence alone.

Your upgrade protocol should look like this:

• Run paired evaluations on your own agent tasks — same prompts, both models, multiple runs — before touching production.
• Gate success on terminal-state checks, not read-backs: Sol performed 3.2–5.8 points more read-back after final writes yet still failed more, so instrument duplicates and extra records directly.
• Consider Terra as the middle tier — OpenAI says it matches GPT-5.5's performance at 2x lower cost, and Elser.ai calls it the most sensible first challenger for general coding.
• Use orchestration: several testers report one model plans while Sol executes, containing its overbuilding tendency.

One more layer matters: the skills and tools your agent uses. A model upgrade changes how agents invoke tools, and published research found roughly 26% of agent skills carry at least one vulnerability — so re-validate your toolkit with any model change. SkillzBench is building exactly this: a free directory of security-scanned skills, sandboxed Lift benchmarks that test each skill with and without installation, and a free MCP connector so any agent only sees the tools you choose for it.

The pattern holds whether you run Sol, GPT-5.5, or Terra: validate the model on your tasks, gate on real end states, and verify the skills layer independently. Leaderboards are context, not a deployment verdict.