In a single 48-hour window in early July 2026, three frontier labs each shipped a model aimed squarely at developers: xAI's Grok 4.5 (July 8), OpenAI's GPT-5.6 family (July 9), and Meta's Muse Spark 1.1 (July 9). If you build coding agents or ship AI features, your default model just got three new options in one week.
This is a practical comparison for engineers, not a leaderboard recap. We line up the three on the numbers that actually change an architecture decision — coding benchmarks, price, context window, token efficiency, and availability — and end with a plain "reach for this one when…" guide. Cross-vendor benchmark figures below are as reported by each lab (mostly xAI's own comparison chart), so treat them as directional, not gospel.
TL;DR
- Grok 4.5 is the coding-agent value pick: $2/$6 per million tokens, strong Terminal-Bench, and roughly 4× better output-token efficiency than Opus 4.8 max — but not in the EU yet.
- GPT-5.6 is a family, not a model: Sol for the hardest reasoning, Terra for balanced cost, Luna for high volume — plus new Responses API agent primitives.
- Muse Spark 1.1 leads on context (1M tokens) and is Meta's first paid API, but it's US-only in preview.
- There is no single winner — pick per workload: reasoning depth (GPT-5.6 Sol), token economics (Grok 4.5), or huge context (Muse Spark).
The three contenders
Grok 4.5 (xAI) — the coding-first value play
Released July 8, Grok 4.5 is xAI's first model built specifically for coding and agentic work. It ships a 500K-token context window, a February 1, 2026 knowledge cutoff, and serves at fast-model speeds (~80 tokens/sec). Pricing is $2 per million input tokens and $6 per million output. It's already wired into Cursor on all plans, Grok Build, and the SpaceXAI console — with one catch: it wasn't available in the EU at launch, with availability expected to follow mid-July.
GPT-5.6 (OpenAI) — one release, three tiers
GPT-5.6 went public July 9 after a customer-by-customer government review. It's a family: Sol (frontier capability), Terra (balanced intelligence and cost), and Luna (efficient, high-volume). All three are reachable through the Responses API and SDKs. On the reasoning side, OpenAI highlighted ARC-AGI-3 progress — Sol was cited as the first model to beat a public game on that benchmark — and the release adds several new developer primitives we break down in our GPT-5.6 Responses API cheat sheet.
Muse Spark 1.1 (Meta) — context king, and Meta's first paid API
Also on July 9, Meta Superintelligence Labs shipped Muse Spark 1.1, a multimodal reasoning model built for agentic tasks with a 1-million-token context window — enough to hold an entire codebase, a full contract set, or months of logs in one call. The bigger story is commercial: it debuts on the new Meta Model API (public preview), the first time Meta charges developers to use its own model. Pricing is $1.25 per million input tokens and $4.25 per million output, US-only at launch.
Coding benchmarks (as reported)
Head-to-head coding numbers across all three were thin at launch — xAI published the most complete chart, so the table below is Grok-centric and uses xAI's reported comparisons. Read it as directional.
| Model | Terminal-Bench 2.1 | DeepSWE 1.0 | SWE-Bench Pro | SWE Marathon |
|---|---|---|---|---|
| Grok 4.5 | 83.3% | 62.0% | 64.7% | 29.0% |
| GPT-5.5 (ref) | 83.4% | 64.31% | — | — |
| Opus 4.8 (ref) | — | 55.75% | 69.2% | 26.0% |
| Fable (ref) | 84.3% | 66.1% | 80.4% | — |
The takeaway isn't "Grok wins" — it trades blows and trails Fable/Opus on SWE-Bench Pro resolve rate. It's that Grok 4.5 lands in the same tier as models costing several times more per token, and it leads the pack on SWE Marathon resolution. GPT-5.6's differentiator is reasoning depth (Sol's ARC-AGI-3 result); Muse Spark's is raw context.
Price & context at a glance
| Model | Input $/M | Output $/M | Context | Availability |
|---|---|---|---|---|
| Grok 4.5 | $2.00 | $6.00 | 500K | Cursor, Grok Build, console (no EU yet) |
| GPT-5.6 Sol | $5.00 | $30.00 | Large | API + ChatGPT |
| GPT-5.6 Terra | $2.50 | $15.00 | Large | API + ChatGPT |
| GPT-5.6 Luna | $1.00 | $6.00 | Large | API + ChatGPT |
| Muse Spark 1.1 | $1.25 | $4.25 | 1M | Meta Model API preview (US-only) |
Why sticker price lies: token efficiency
Per-token price is only half the bill. What you actually pay is price × tokens generated, and agentic coding burns output tokens on reasoning, tool calls, and diffs. xAI reports Grok 4.5 resolves an average SWE-Bench Pro task in about 15,954 output tokens versus roughly 67,020 for Opus 4.8 at max effort — about 4.2× fewer. A cheaper-per-token model that also emits fewer tokens compounds the savings. We work the math in Coding-Agent Token Economics.
Which should you pick?
- Reach for Grok 4.5 when you're running coding agents at volume, care about cost-per-task, or already live in Cursor — and you're not blocked by the EU gap.
- Reach for GPT-5.6 when the task is hard enough to justify Sol's reasoning, when you want a cost/quality dial (Terra vs Luna) inside one API, or when you need the new multi-agent orchestration and programmatic tool calling in the Responses API.
- Reach for Muse Spark 1.1 when the job genuinely needs 1M-token context — whole-repo reasoning, long document sets, months of logs — or computer-use agents, and you're operating in the US.
Caveats before you commit
- Availability gates the decision. Grok 4.5 wasn't in the EU at launch; Muse Spark 1.1 is US-only preview. Check your region before you design around either.
- Cross-vendor benchmarks are directional. Most numbers here are self-reported by the labs. Validate on your own eval set before switching production traffic.
- These are launch prices. Preview pricing (Muse) and new tiers often move. Re-check before you model unit economics.
The healthy way to read this week isn't "which model won." It's that frontier coding capability now comes in three shapes — cheap-and-efficient, reasoning-deep, and context-huge — and the engineering skill is matching the shape to the workload.
