--- title: "Grok 4.6 vs Claude Opus 5 vs Fable 5 vs GPT-5.6 Sol (2026)" description: "Four frontier models, one benchmark score. Grok 4.6, Claude Opus 5, Claude Fable 5, and GPT-5.6 Sol compared on price, context window, coding benchmarks, and cost per task — and which one you should actually build on." source: https://appstackbuilder.com/blog/grok-4-6-vs-claude-opus-5-vs-fable-5-2026 retrieved: 2026-08-18 --- [Back to Blog](https://appstackbuilder.com/blog) AI DevelopmentAugust 2026•13 min read # Grok 4.6 vs Claude Opus 5 vs Fable 5 vs GPT-5.6 Sol (2026) Four frontier models shipped inside five weeks, and three of them land on the same headline number: **61 on the Artificial Analysis Intelligence Index**. When the leaderboard stops separating models, price, context, and workflow fit decide for you. Here's what actually differs between Grok 4.6, Claude Opus 5, Claude Fable 5, and GPT-5.6 Sol. ## Quick Comparison | Feature | Grok 4.6 | Claude Opus 5 | Claude Fable 5 | GPT-5.6 Sol | | --------------------- | ------------------ | -------------- | ----------------- | ---------------------- | | Maker | xAI | Anthropic | Anthropic | OpenAI | | Context window | 500K | 1M | 1M | 1M | | Input / output per 1M | $2 / $6 | $5 / $25 | $10 / $50 | $5 / $30 | | AA Intelligence Index | 61 | 61 | 60–65\* | 61 (max) | | SWE-bench Verified | Not headline | \~96% | \~95% | Top-tier | | Best at | Cost per agent run | Agentic coding | Hardest reasoning | Cost-adjusted frontier | \*Published Fable 5 index scores vary between 60 and 65 depending on the reasoning-effort setting and the run date. Treat all benchmark figures here as directional. ## The short version The interesting story of August 2026 is not which model is smartest. Three of these four sit at 61 on the same composite index, and the fourth is a point or two above depending on whose run you read. The story is **how differently they're priced for the same measured intelligence** — a 5× spread between the cheapest and most expensive option here. * **Grok 4.6** — frontier-tier scores at $2 / $6\. The cheapest way to run a long-horizon agent loop right now, with a 500K context window as the tradeoff. * **Claude Opus 5** — the strongest agentic coder of the four, leading SWE-bench Verified at \~96%, at half Fable 5's price. * **Claude Fable 5** — the ceiling for the hardest reasoning, and priced like it at $10 / $50\. Reach for it when correctness beats cost. * **GPT-5.6 Sol** — near-parity with the frontier at roughly a third of Fable 5's cost per task, with a reasoning-effort dial that lets you buy exactly as much intelligence as you need. ## Grok 4.6: the price disruptor xAI shipped Grok 4.6 on August 12, 2026 — and notably, it's a **post-training upgrade over Grok 4.5, not a bigger base model**. The gains come from a longer supplemental training run, regenerated supervised fine-tuning trajectories, and reinforcement learning in agentic environments. That matters because it means the improvement is concentrated exactly where xAI trained it: long-running agent work. The benchmark that moved most is Terminal-Bench v3.0, which jumped from 15.7% on Grok 4.5 to **26%** — nearly double. It also posts 69.9% on CursorBench v3.2, 61.3% on FrontierCode v1.1 Extended, and 57.5% on APEX-Agents. On the Artificial Analysis Intelligence Index it scores 61, up five points from 4.5 and level with GPT-5.6 Sol at max reasoning. Pricing is the headline: **$2 input / $0.50 cached input / $6 output per 1M tokens** below 200K prompt tokens, rising to $4 / $1 / $12 above that threshold. Against Claude Fable 5's $10 / $50, that's roughly a fifth of the price for a comparable index score. The model also adds an `xhigh` reasoning-effort level above the ladder Grok 4.5 shipped with, and it's live in Cursor and Grok Build today. The real constraint is the **500K-token context window** — half of what the other three offer. For most agent loops that is plenty; for whole-monorepo analysis or long document pipelines, it's the thing that will push you elsewhere. Note too that the price steps up above 200K prompt tokens, so a long-context workload doesn't stay cheap at the sticker rate. ## Claude Opus 5: the agentic coding leader Anthropic launched Claude Opus 5 on July 24, 2026, positioning it as close to Fable 5's frontier intelligence at roughly half the price. It ships at **$5 input / $25 output per 1M tokens** with a 1M-token context window (the maximum is also the default) and 128K max output. On coding it currently leads: **\~96% on SWE-bench Verified** by Anthropic's launch figure, with an independent leaderboard run reporting 97%, plus the lead on SWE-bench Pro. Beyond coding it posts 43.3% on Frontier-Bench (agentic coding), 30.2% on ARC-AGI-3 (novel reasoning — roughly 3× the next model), and a 1,861 Elo on GDPval-AA v2 for knowledge work. Its Intelligence Index score of 61 put it at #1 of 170 models at launch. Two practical details matter if you're migrating. First, **thinking is on by default** — omitting the thinking parameter runs adaptive thinking, where the previous generation ran without it. Since your max output token budget covers thinking plus the answer, a request sized tightly around its response on Opus 4.8 can truncate on Opus 5\. Second, the minimum cacheable prompt drops to 512 tokens (from 1024), so prompts you'd written off as too short to cache now cache with no code change. This is also the model behind [Claude Code](https://appstackbuilder.com/blog/cursor-vs-claude-code-2026), which is the clearest signal of where its strength lies: multi-file refactors, migrations, and autonomous runs that finish without hand-holding. ## Claude Fable 5: the ceiling, priced like it [Claude Fable 5](https://appstackbuilder.com/blog/claude-fable-5-mythos-5-launch-2026) is Anthropic's most capable widely released model, aimed at the most demanding reasoning and long-horizon agentic work. It runs $10 input / $50 output per 1M tokens — double Opus 5 — with a 1M-token context window and 128K max output. It scores roughly 95% on SWE-bench Verified and around 80% on SWE-bench Pro. What you get for the premium is the top of the reasoning curve on genuinely hard problems, plus an effort ladder that runs all the way to `max`. What you should plan for is different from a normal model swap: * **Thinking is always on**, and the raw chain of thought is never returned — you get summaries, not reasoning traces. * **Turns run long.** A single request on a hard task can take many minutes. Budget for streaming, timeouts, and async check-ins rather than blocking inside one call. * **It requires 30-day data retention** and is not available under zero data retention — a hard blocker for some compliance setups. * **Cache pricing doubles too** ($1/M reads, $12.50/M writes), so cache-heavy workloads don't escape the premium. Fable 5 was also the most expensive model Artificial Analysis has ever benchmarked — roughly $6.2K to run their Intelligence Index once. That number is a useful proxy for what "run everything on the best model" costs at scale. ## GPT-5.6 Sol: buy exactly the intelligence you need GPT-5.6 Sol is the top tier of OpenAI's [GPT-5.6 lineup](https://appstackbuilder.com/blog/gpt-5-6-vs-claude-vs-gemini-2026) (Sol, Terra, Luna), at **$5 input / $30 output per 1M tokens** with a 1M-token context window, 128K max output, and a February 2026 knowledge cutoff. Sol's distinguishing feature is the reasoning-effort dial, and its Intelligence Index score moves with it: **61 at max, 59 at xhigh, 57 at high, 56 at medium**. That's a genuinely useful property — you can route easy traffic to medium and hard traffic to max on the same model, without swapping providers or re-tuning prompts. On cost-adjusted terms it's the strongest showing of the four against Fable 5: Sol at max reasoning costs about **$1.04 per task** on the Artificial Analysis Intelligence Index and lands within a point of Fable 5 — roughly one-third the cost for effectively the same measured result. ## Benchmarks side by side | Benchmark | Grok 4.6 | Claude Opus 5 | Claude Fable 5 | GPT-5.6 Sol | | ------------------------------- | --------------------- | ----------------- | --------------------- | ---------------------- | | AA Intelligence Index | 61 | 61 (#1 at launch) | 60–65 (varies by run) | 61 (max) / 56 (medium) | | SWE-bench Verified | Not a headline number | \~96% | \~95% | Top-tier | | Terminal-Bench v3.0 | 26% (from 15.7%) | — | — | — | | Frontier-Bench (agentic coding) | — | 43.3% | — | — | | ARC-AGI-3 (novel reasoning) | — | 30.2% | — | — | | Context window | 500K | 1M | 1M | 1M | Vendors publish different benchmark suites, so blank cells mean "not reported by that vendor," not zero. Every number here comes from vendor or third-party reports as of August 2026 and moves quickly. The gaps at the top are small — an eval on your own workload is worth more than any leaderboard. ## Pricing comparison | Model | Input / 1M | Output / 1M | Best for | | -------------- | ---------- | ----------- | -------------------------------------- | | Grok 4.6 | $2 | $6 | Cheapest frontier-tier agent loop | | Claude Opus 5 | $5 | $25 | Agentic coding, long refactors | | Claude Fable 5 | $10 | $50 | Hardest reasoning, cost no object | | GPT-5.6 Sol | $5 | $30 | Tunable effort, cost-adjusted frontier | Grok 4.6 rises to $4 / $12 for prompts over 200K tokens. Fable 5 cache pricing is $1/M reads and $12.50/M writes. All figures are list API rates as of August 2026. ## Sticker price isn't your bill Two things distort the table above, and both favor careful measurement over leaderboard reading. **Reasoning tokens are output tokens.** Every model here thinks before it answers, and you pay output rates for that thinking. A model that reasons twice as long at half the output price is a wash. This is why cost-per-task figures — like Sol's \~$1.04 on the AA Intelligence Index — tell you more than per-token rates do. **Caching changes the ranking.** Agent loops resend a large stable prefix on every turn. With prompt caching, cached input is where most of your spend lands: Grok 4.6 reads cached input at $0.50/1M, while Fable 5 charges $1/1M for reads and $12.50/1M for writes. If your workload is prefix-heavy, run the numbers on cached-read pricing rather than the headline input rate. ## Which should you use? ### Choose Grok 4.6 if: * You're running high-volume agent loops and cost per run is the binding constraint * Your prompts fit comfortably under 200K tokens, where the cheap tier applies * You work in Cursor or Grok Build and want frontier-tier scores without frontier-tier billing ### Choose Claude Opus 5 if: * Your work is agentic coding: multi-file refactors, migrations, autonomous runs * You want the current SWE-bench Verified leader without paying Fable 5 rates * You need a full 1M-token context at a mid-tier price ### Choose Claude Fable 5 if: * You're working at the edge of what models can do and correctness beats cost * Long, minutes-per-turn autonomous runs are acceptable in your product * Your compliance posture allows 30-day data retention ### Choose GPT-5.6 Sol if: * You want one model that spans easy and hard traffic via a reasoning-effort dial * You're optimizing cost per task rather than cost per token * You're already on OpenAI and want Terra and Luna one config change away ## The verdict **For most teams building in late 2026, Grok 4.6 is the value story and Claude Opus 5 is the quality default.** Grok 4.6 delivers a frontier-tier index score at a fraction of the price, which makes agent architectures that were uneconomic three months ago viable today — provided 500K of context is enough. Opus 5 is where you go when the work is real engineering: it leads SWE-bench Verified and holds a full 1M context at half Fable 5's cost. **GPT-5.6 Sol is the best cost-adjusted frontier option** if you want one model and one dial across a range of task difficulty. And **Fable 5 remains the ceiling** — the right call when a wrong answer costs more than the tokens, and the wrong call for anything routine. The honest takeaway is the same as it was in July, only sharper: the frontier has converged, and the differences that remain are economic and operational rather than intellectual. Pick two, run them against your own workload for a week, and let your own numbers decide. ### Build Your Perfect Tech Stack Picking a model is one decision — the rest of your stack matters too. Let our AI recommend the right tools for your project, budget, and goals. 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