GPT-5.6 Sol vs Claude Opus 5 vs Gemini 3.6
Which frontier model should you be paying for in August 2026? Six weeks after the July release wave, the benchmark dust has settled enough to give a real answer.
- Best all-round quality: Claude Opus 5 or GPT-5.6 Sol — separated by less than a benchmark point on most tasks, so cost and integration usually decide.
- Best for coding agents: Claude Opus 5 (86.7% Terminal-Bench inside Claude Code; #1 on the Artificial Analysis Agentic Index at 55.3).
- Best for long-context research and multimodal reasoning: Gemini 3.1 Pro (2M-token window, cheap up to 200k).
- Best cheap workhorse: GPT-5.6 Terra ($2/$12/M after the 30 July price cut) or Claude Sonnet 5 ($2/$10/M intro through Aug 31, then $3/$15).
- Best ultra-budget: GPT-5.6 Luna ($0.20/$1.20/M) — 80% cheaper than it was six weeks ago.
Why this comparison matters right now
Three model families all shipped within about a month of each other:
- OpenAI GPT-5.6 family (Sol, Terra, Luna) released 9 July 2026. Terra and Luna got significant price cuts on 30 July.
- Anthropic Claude 5 family (Sonnet 5, Fable 5, Opus 5, and Mythos 5) — four models in under two months, with Opus 5 landing 24 July.
- Google Gemini 3.6 Flash shipped in July as the new fast tier; Gemini 3.1 Pro remains the Pro flagship.
By early August the third-party benchmark aggregators (Artificial Analysis, LM Arena, LM Council, Terminal-Bench) had enough runs to be trustworthy. The picture is clearer than it was in mid-July — this is a good moment to pick.
Benchmarks (August 2026)
Three numbers matter for most builders — a general-quality index, a coding score, and something long-context. Here's where the top-tier models sit:
| GPT-5.6 Sol | Claude Opus 5 | Gemini 3.1 Pro | |
|---|---|---|---|
| Artificial Analysis Intelligence Index | high 50s | 63 | mid 50s |
| Artificial Analysis Agentic Index | 54.0 | 55.3 | low 50s |
| Terminal-Bench 2.1 (coding, raw) | 89.5% | 89.1% | low 80s |
| Terminal-Bench inside its native agent | n/a (Codex CLI) | 86.7% (Claude Code) | n/a |
| Long-context comprehension | strong to 400k | strong to 1M | strong to 2M |
How to read this. Opus 5 and GPT-5.6 Sol are within a percentage point of each other on nearly every general-quality benchmark. Opus 5 leads by a bit on multi-turn agentic tasks; GPT-5.6 Sol edges it out on raw single-shot coding. Gemini 3.1 Pro doesn't top either but has the widest capability envelope — 2M-token context is unmatched, and it consistently wins on reasoning-heavy research workloads.
Pricing (per 1M tokens, verified 9 August 2026)
| Model | Input | Output | Context | Notes |
|---|---|---|---|---|
| GPT-5.6 Sol OpenAI | $5.00 | $30.00 | 400k | Flagship |
| GPT-5.6 Terra OpenAI | $2.00 | $12.00 | 400k | Cut from $2.50/$15 on 30 July |
| GPT-5.6 Luna OpenAI | $0.20 | $1.20 | 400k | 80% cheaper than pre-July |
| Claude Fable 5 Anthropic | $10.00 | $50.00 | 1M | Research tier |
| Claude Opus 5 Anthropic | $5.00 | $25.00 | 1M | #1 Agentic Index |
| Claude Sonnet 5 Anthropic | $2.00 | $10.00 | 1M | Intro rate through 31 Aug; $3/$15 from 1 Sep |
| Claude Haiku 4.5 Anthropic | $1.00 | $5.00 | 200k | Small, fast |
| Gemini 3.1 Pro Google | $2.00 / $4.00 | $12.00 / $18.00 | 2M | Second tier above 200k prompt |
| Gemini 3.6 Flash Google | $1.50 | $7.50 | 1M | New — July 2026 |
Every price above is checkable live in our Token Counter & Cost Estimator — paste text, pick a model, get the round-trip cost. Same MODELS list, same verification date.
What each one is actually best at
Claude Opus 5
Why: #1 on the Artificial Analysis Agentic Index (55.3). Best Terminal-Bench score when run inside its native Claude Code agent (86.7%). The 1M-token context lets it hold a whole mid-sized codebase in mind at once — this is what makes it feel qualitatively different from Sonnet.
Watch for: Output tokens at $25/M add up faster than they look. Long agentic runs where the model reasons for 5k tokens per step are ~2x what they cost on Sonnet 5, so put reasoning-heavy work behind a "planner → executor" split where the executor is Sonnet.
GPT-5.6 Sol
Why: Roughly tied with Opus 5 on general quality (within a benchmark point), edges it out on raw single-shot coding (89.5% Terminal-Bench vs 89.1%). If you're already on OpenAI's platform, the Assistants API / responses API / structured outputs / function-calling ergonomics are the most mature in the industry. Cheaper input tokens than Opus ($5 vs $5 flat is a wash, but you'll typically send more context to Opus for the same task).
Watch for: The output-token price at $30/M is the highest of any current non-research-tier model — a hair more expensive per output token than Opus 5. If your workload is output-heavy (long generation) rather than input-heavy (long context), Opus 5 wins on cost.
Gemini 3.1 Pro
Why: 2M-token context nobody else has. Cheap at $2/$12 up to 200k tokens. Vision quality is the best of the three on document extraction and chart reading. Gemini's Deep Research mode remains the strongest for "read 40 PDFs and synthesise" workloads in one call.
Watch for: Above 200k input tokens, pricing jumps to $4/$18 — a 2x cost cliff. Design your prompts to stay under 200k if you can. Also weaker than Opus 5 / GPT-5.6 Sol on multi-step agentic tasks that require tool calls with strict schemas.
GPT-5.6 Terra ($2/$12) or Claude Sonnet 5 ($2/$10 intro)
Why: Both are frontier-adjacent (typically 5-10 quality points below the flagships) at roughly 40% of flagship price. Terra had its price cut on 30 July which effectively doubled its cost-effectiveness overnight. Sonnet 5's intro pricing runs through 31 August — worth building against it now, budgeting for the September 1 step to $3/$15.
Watch for: Sonnet 5's price jump on 1 Sep will surprise anyone who signs up in August without reading the fine print. Set a September calendar reminder to review whether you're still on the right tier.
GPT-5.6 Luna ($0.20/$1.20) or Gemini 3.6 Flash ($1.50/$7.50)
Why: After the July price cut, Luna is the cheapest capable frontier-family model on the market by a wide margin — 80% cheaper than the pre-cut price. Good enough for classification, extraction, tagging, simple summarisation. Gemini 3.6 Flash is a bit pricier but is stronger on structured extraction from long docs.
Watch for: Do not use these for complex reasoning or multi-step agents. The quality drop-off is real.
Cost per task — a real example
To make this concrete: assume a customer-support agent that handles ~3,000 tickets per day. Each ticket includes ~1,500 tokens of context (customer history, product docs) and generates a ~500-token reply. So per day: 4.5M input tokens, 1.5M output tokens.
| Model | Input cost/day | Output cost/day | Total/day | Total/month |
|---|---|---|---|---|
| GPT-5.6 Sol | $22.50 | $45.00 | $67.50 | $2,025 |
| Claude Opus 5 | $22.50 | $37.50 | $60.00 | $1,800 |
| GPT-5.6 Terra | $9.00 | $18.00 | $27.00 | $810 |
| Claude Sonnet 5 (intro) | $9.00 | $15.00 | $24.00 | $720 |
| Claude Sonnet 5 (from Sep 1) | $13.50 | $22.50 | $36.00 | $1,080 |
| Gemini 3.6 Flash | $6.75 | $11.25 | $18.00 | $540 |
| GPT-5.6 Luna | $0.90 | $1.80 | $2.70 | $81 |
Read this table twice. For a workload that could plausibly be served by any of these, running on GPT-5.6 Sol costs 25x what Luna costs. Whether the quality difference is worth 25x depends entirely on your quality bar. Most customer-support workloads work fine on Sonnet 5 or Terra; some are fine on Flash; a few genuinely need the frontier tier.
Two-tier routing is worth building — use Luna / Flash to attempt every ticket, and only escalate the ones where the model reports low confidence to Opus / Sol. You'll typically end up at 30-50% of the "always use flagship" cost for the same customer-visible quality.
What each one stumbles on
- GPT-5.6 Sol: still verbose on refusals compared to Opus 5. Instruction following is very literal, which is a feature or a bug depending on the task.
- Claude Opus 5: expensive output tokens ($25/M) mean it can quietly cost 2x what you expected on reasoning-heavy workloads. Also more likely than GPT-5.6 to refuse edge-case red-team style requests.
- Gemini 3.1 Pro: the 200k-token price cliff is a landmine — set a hard prompt-length cap or you'll get surprise invoices. Agentic tool calling with strict schemas is a step behind Anthropic and OpenAI.
- All three: prompt injection via untrusted content in the context is still an unsolved problem — see the 2026 Prompt Injection Casebook for the 13 patterns still landing against all frontier models.
Our pick, if we had to pick one
Claude Opus 5 for anything agentic, GPT-5.6 Terra for the workhorse tier, Gemini 3.1 Pro for research and long-context. That combination costs less than sitting entirely on one flagship and gives you the best of each family on the tasks each is best at.
If you can only pay for one vendor: Anthropic, in August 2026. Opus 5 at the top and Sonnet 5's intro pricing at the workhorse tier is the strongest single-family combo right now. Ask again in three months — this leaderboard is not stable.
Related tools and posts
- LLM Token Counter & Cost Estimator — every model above is in the dropdown with live pricing.
- The 2026 Prompt Injection Casebook — 13 patterns still landing against frontier models.
- MCP Inspector — for teams wiring Opus 5 or GPT-5.6 to MCP tool servers.
- The EU AI Act after August 2, 2026 — GPAI obligations for people running any of these in production for EU users.