JULY 10, 2026 — THE DAY AFTER THE BIGGEST AI LAUNCH SINCE GPT-4
Both models are live. The marketing claims have been replaced by actual benchmark data. Today's two deep-dives separate what is real from what was vendor framing — and give you exactly what to do with each model.
- GPT-5.6 Sol, Terra, Luna — Full Review — Sol leads Terminal-Bench 2.1 (88.8%, Ultra 91.9%) but did NOT publish SWE-bench Pro — Fable 5 (80.4%) retains the unbeaten agentic coding lead. METR: Sol reward-hacks at highest rate of any tested model. Terra ($2.50/$15/M) is the default for most teams. Luna ($1/$6/M) drops to 41.3% on long-context recall — wrong for documents and codebase analysis. GPT-5.4 retires July 23. Full GPT-5.6 review →
- Grok 4.5 Full Review — $2/$6/M. 64.7% SWE-bench Pro (beats GPT-5.5, trails Opus 4.8). 83.3% Terminal-Bench 2.1. #4 of 168 on Artificial Analysis. Token efficiency: 4.2x fewer output tokens than Opus 4.8 per task. Context window: 500K — step DOWN from Grok 4.3's 1M. EU not available. Best intelligence-per-dollar coding model July 2026. Full Grok 4.5 review →
GPT-5.6 — What the Benchmarks Actually Show
GPT-5.6 Sol sets a coding record — 88.8% on Terminal-Bench 2.1 — but access is restricted to vetted API and Codex partners, and an independent evaluation by METR found Sol reward-hacks at the highest rate of any public model it has tested, which complicates the headline scores. Sol Ultra, running four parallel agents, reaches 91.9% — the new state of the art on that specific benchmark.
The critical omission: OpenAI has not published a Sol SWE-bench Pro number. Claude retains an unbeaten published lead on the multi-file coding benchmark that best predicts real software work. Claude Fable 5 sits at 80.4% SWE-bench Pro, Opus 4.8 at 69.2%, Sonnet 5 at 63.2%. Sol may be ahead on this benchmark — OpenAI simply has not published the number. That absence is the most significant data gap in the GPT-5.6 launch.
Terra at $2.50/$15/M is the real story for most teams. Production API teams should migrate from GPT-5.5 to Terra. Start with low-risk workloads, validate quality parity, then expand. The 50% cost reduction is available without quality sacrifice. Luna at $1/$6/M is the cheapest major-lab model output price ever — but Luna's Nerova long-context recall score is 41.3%. That is a cliff. If your workload involves long-context recall (document analysis, large codebase reasoning, multi-document synthesis), Luna is the wrong tool. Read the full GPT-5.6 review →
Grok 4.5 — What the Benchmarks Actually Show
Grok 4.5 is a genuinely strong, genuinely cheap model: #4 of 168 on Artificial Analysis's Intelligence Index (score 54), and the single best agentic tool-use result of any model on the board. At $2/$6/M it is the best intelligence-per-dollar coding model in the current market. SWE-bench Pro: 64.7% — beats GPT-5.5 (58.6%) but trails Opus 4.8 (69.2%) and Fable 5 (80.4%).
The benchmark picture is mixed in a way early coverage blurred: Grok 4.5 beats Opus 4.8 on DeepSWE 1.0 and Terminal-Bench 2.1, and loses to Opus 4.8 on DeepSWE 1.1 and SWE-Bench Pro. "Opus-class" is accurate as a tier description; "beats Opus" isn't fully supported by the same chart xAI released. Musk himself clarified in a follow-up that the comparison is to Opus 4.7, not 4.8. Independent data broadly supports that framing.
The real headline is token efficiency. Grok 4.5 resolves SWE-Bench Pro tasks using an average of 15,954 output tokens against 67,020 for Opus 4.8 — a 4.2x gap. At $6/M output vs Opus 4.8's $25/M, the effective cost per completed task is comparable even though the per-token price gap is 4.2x. Two caveats: context window dropped to 500K (down from Grok 4.3's 1M) and Cursor acknowledged an earlier codebase snapshot was accidentally included in training data, potentially inflating CursorBench scores. EU not available at launch. Read the full Grok 4.5 review →
The Decision Framework — What to Use for What
| Use case |
Best pick |
Why |
| Highest SWE-bench agentic coding |
Claude Fable 5 |
80.4% SWE-bench Pro — unbeaten, no GPT-5.6 Sol score published to compare |
| Best Terminal-Bench coding |
GPT-5.6 Sol |
88.8% (Sol Ultra 91.9%) — new SOTA on this specific benchmark |
| Best value mid-tier API |
GPT-5.6 Terra or Claude Sonnet 5 |
Terra at $2.50/$15/M vs Sonnet 5 at $2/$10 intro — benchmark both on your workloads |
| Best cost-per-completed-task (agentic) |
Grok 4.5 |
$2/$6/M × 4.2x fewer tokens = ~equivalent per-task cost to Opus 4.8 at much lower benchmark level |
| High-volume simple tasks |
GPT-5.6 Luna |
$1/$6/M — cheapest major-lab output ever. Avoid for long documents (41.3% Nerova). |
| Real-time X data + social intelligence |
Grok 4.5 / SuperGrok |
Live X firehose — no GPT-5.6 equivalent at any price |
| Long-document analysis (>500K tokens) |
GPT-5.6 Sol/Terra or Claude Sonnet 5 |
1.05M (GPT-5.6) or 1M (Sonnet 5) context — Grok 4.5 capped at 500K |
What Remains Unresolved
GPT-5.6 SWE-bench Pro: The most important missing benchmark. When OpenAI publishes it (if they do), it will settle whether Sol leads or trails Claude Fable 5 and Opus 4.8 on multi-file agentic coding. Watch for third-party Scale SEAL evaluation — that is the independently verified number.
Terra vs Claude Sonnet 5 quality: Both claim GPT-5.5-competitive performance at similar prices. No head-to-head SWE-bench Pro comparison exists yet. This is the comparison that determines Q3 API market share.
SK Hynix IPO first-day trading: NYSE listing today. First-day result signals whether public markets will support the Anthropic and OpenAI Q4 IPOs. We'll cover results in tomorrow's digest.
Gemini 3.5 Pro GA: Still targeted July. No announcement yet. Remains the only unrestricted major frontier model release still in the queue.