When xAI shipped Grok 4.5 on July 8, 2026, Elon Musk picked the comparison himself: “an Opus-class model, but faster, more token-efficient and lower cost.” That’s a specific, checkable claim about Claude Opus 4.8, Anthropic’s workhorse coding model.
So let’s check it. This comparison uses the numbers xAI published in its announcement, Anthropic’s published pricing, and the parts of the story neither vendor puts in a headline. The short version: the claim mostly holds, with two benchmark losses and one large asterisk about verification.
The scorecard
xAI published four coding benchmarks. Here they are, with both models plus the frontier context around them:
| Benchmark | Grok 4.5 | Opus 4.8 (max) | Winner |
|---|---|---|---|
| DeepSWE 1.0 (pass@1) | 62.0% | 55.75% | Grok 4.5 (+6.25) |
| DeepSWE 1.1 | 53% | 59% | Opus 4.8 (+6) |
| Terminal Bench 2.1 | 83.3% | 78.9% | Grok 4.5 (+4.4) |
| SWE Bench Pro (resolve) | 64.7% | 69.2% | Opus 4.8 (+4.5) |
Two wins each. On xAI’s own charts, “Opus-class” is accurate as a capability tier and wrong as a claim of superiority, which, to xAI’s credit, isn’t the claim Musk made.
Worth noting: Claude Fable 5 (max) tops all four of these charts (66.1 / 70 / 84.3 / 80.4), and GPT 5.5 (xhigh) beats both models on three of the four. Grok 4.5 is competing in the tier below the frontier, against the model Anthropic prices for daily work rather than peak capability. If you want the frontier fight, that’s Fable 5 vs Opus 4.8.
The provenance asterisk
These aren’t independent test results. xAI notes competitor figures come from “the respective developers’ published system cards or benchmark leaderboards,” with the DeepSWE evals created by Datacurve and runs handled with each provider’s harness. That’s better than opaque self-reporting, but mixing sources means harness and scaffold differences leak into the comparison. No fully independent third party has benchmarked Grok 4.5 yet. Our benchmarks deep dive tracks that situation.
Price: Grok wins on paper, bigger in practice
Sticker prices per million tokens:
| Grok 4.5 | Opus 4.8 | |
|---|---|---|
| Input | $2.00 | $5.00 |
| Output | $6.00 | $25.00 |
That’s 40% of Opus’s input price and 24% of its output price. (Full Anthropic-side detail in our Opus 4.8 pricing breakdown.)
The gap widens when you factor in verbosity. xAI reports Grok 4.5 resolves SWE Bench Pro tasks with an average of 15,954 output tokens; Opus 4.8 (max) averages 67,020 on the same benchmark. Multiply it out per resolved task:
- Grok 4.5: 15,954 tokens × $6/M ≈ $0.10 of output per task
- Opus 4.8 (max): 67,020 tokens × $25/M ≈ $1.68 of output per task
Roughly 17x cheaper on output per completed task, per the vendor-reported token counts. Treat the exact multiple with caution (one benchmark, one vendor’s measurement, and “max” effort mode inflates Opus’s token count), but the direction is hard to argue with: a terse model at $6 beats a verbose model at $25 by more than the price sheet suggests. We run fuller scenarios in Grok 4.5 pricing explained.
The caveat cuts the other way too: Opus loses more tokens per task partly because “max” mode reasons at length, and that reasoning is part of why it wins SWE Bench Pro by 4.5 points. You’re paying for thinking. Sometimes the thinking is the product.
Speed: 80 TPS and shorter answers compound
Grok 4.5 serves at about 80 tokens per second, which xAI calls “fast-model speeds.” Combined with outputs that are a quarter of the length, wall-clock time per task drops twice: fewer tokens, delivered faster. For agent loops that chain dozens of model calls, latency per step compounds into minutes saved per session.
Anthropic doesn’t publish an equivalent TPS figure for Opus 4.8, and real-world speeds vary by load and tier, so benchmark this on your own traffic rather than trusting either marketing page.
Where Opus 4.8 keeps the edge
Price isn’t everything, and the scorecard names the places it isn’t:
- The hardest agentic coding. SWE Bench Pro, the closest of the four to messy real-repo work, goes to Opus by 4.5 points. DeepSWE 1.1, the newer revision, goes to Opus by 6.
- Ecosystem maturity. Opus 4.8 slots into Claude Code, established tool-use patterns, and a year of production hardening. Grok 4.5 launched yesterday; its integration surface is Grok Build, Cursor, and a fresh API. Migration cost is real even when per-token cost is lower.
- Predictability. Opus’s behavior under long contexts, its refusal patterns, and its failure modes are well documented, including by us. Grok 4.5’s are a week old.
Which should you use?
Pick Grok 4.5 if your workload is high-volume agentic coding or knowledge work, your API bill is a line item you watch, and a 2-of-4 benchmark split at a quarter of the output price sounds like arbitrage. Launch pricing plus the current free windows make trying it nearly costless this month.
Stay on Opus 4.8 if you’re deep in the Anthropic ecosystem, you need the strongest published scores on hard repo-level benchmarks in this price class, or you can’t accept week-one model risk in production.
Either way, measure it yourself. Both APIs speak OpenAI-compatible shapes, so an A/B harness is cheap to build. In Apidog, save one request per model, point them at your five hardest real prompts, and compare output quality, latency, and the usage object side by side. Assert on output_tokens specifically: if Grok’s answers on your prompts aren’t shorter, the economics above don’t apply to you. Download Apidog free and run the comparison on your own traffic before moving a workload.
Setup guides for both sides: how to use the Grok 4.5 API and how to use the Claude Opus 4.8 API.
FAQ
Is Grok 4.5 better than Claude Opus 4.8? They split xAI’s published benchmarks 2-2. Grok 4.5 is cheaper and more token-efficient; Opus 4.8 wins the two harder repo-level evals. “Better” depends on whether your constraint is budget or peak capability.
How much cheaper is Grok 4.5 than Opus 4.8? $2 vs $5 per million input tokens and $6 vs $25 per million output. Per resolved coding task, vendor-reported token counts suggest a much larger effective gap.
Are there independent benchmarks for Grok 4.5? Not yet. Published figures mix xAI’s runs, Datacurve evals, and other vendors’ system cards. Independent numbers should land within weeks of launch.
Can I test both models with the same code? Mostly, yes. Both expose OpenAI-compatible chat completions, so swapping base_url, key, and model ID covers the basics. Tool-calling and structured-output details differ; test those paths explicitly before switching.
