Grok 4.3 vs Claude Opus 4.8: Cost and Speed vs Capability (2026)

By mid 2026 the question for engineering teams is rarely which model is the single smartest. It is which axis your workload actually pays for. xAI's Grok 4.3, released May 6 2026, optimizes one axis hard: price per token and raw speed, while landing near the frontier on general capability with a 1M token context window and native video input. Anthropic's Claude Opus 4.8, released May 28 2026, optimizes the other: it ships a full published benchmark suite and leads where measured, and it is the most reliable option we have tested for multi step tool use and surgical patches. They are not trying to win the same fight.

One thing to flag up front, because it shapes how this comparison has to be read: Anthropic's Opus 4.8 launch table did not benchmark Grok 4.3 directly, and xAI did not publish SWE-Bench Pro, Terminal-Bench 2.1, or OSWorld-Verified scores in a form that lines up against Anthropic's numbers. So the head to head is asymmetric. It is strongest on the things both vendors do report: list pricing, context window, speed, and the one knowledge work benchmark with a reported cross point, GDPval-AA. We show Opus 4.8's full benchmark suite below for reference, but we do not invent matching Grok figures where none were published.

On the dimensions we can verify, the split is clean. Grok 4.3 is dramatically cheaper (input at one quarter of Opus, output at one tenth), fast, and the only one of the two with native video. Opus 4.8 leads the one place both are measured (GDPval-AA, 1890 against about 1500) and carries the published capability ceiling. So Grok 4.3 is the cost and speed play for high volume agentic workloads, and Opus 4.8 is the capability ceiling. Below is the head to head, including a cost framing and a routing pattern for teams that want both.

Comparison Table: Grok 4.3 vs Opus 4.8

List Pricing Is the Verified Backbone

The benchmark score percentage is the wrong unit to budget against. A capability number tells you the share of issues a model can close, but it says nothing about what each closed issue cost you. For agentic workloads the number that hits the invoice is what you spend per unit of useful output, and the verified starting point for that is list pricing.

On list pricing the gap is stark and it is not an estimate. Grok 4.3 charges $1.25 per 1M input tokens and $2.50 per 1M output tokens. Opus 4.8 charges $5 per 1M input and $25 per 1M output at the standard tier. So Grok's input is one quarter of Opus and its output is one tenth. That single fact is the backbone of the cost case: for the same token volume, a Grok 4.3 run is far cheaper, and the gap is widest on output heavy agentic loops where the 10x output spread dominates.

The picture gets a little more favorable to Grok once you fold in failure behavior, though here we are into editorial estimates rather than published numbers. In our testing Grok's failures tend to be quick failures: it gives up or returns a wrong patch faster, so it burns fewer tokens on the issues it cannot close. We estimate that keeps its effective cost per resolved task low even when its resolution rate trails, because you pay less per attempt and less per failure. Treat that as our read from internal runs, not a vendor figure.

Walk it through on a realistic run. Suppose you point coding agents at a 500 issue backlog. The verified driver is the per token price: at one quarter the input and one tenth the output, the same token spend on Grok 4.3 costs a fraction of the Opus 4.8 equivalent. We estimate Opus closes a higher share of the backlog and Grok closes most of what Opus does, so the practical tradeoff in our runs is a modest dip in resolved count for a large drop in spend. The exact split depends entirely on your task distribution, which is why the honest move is to measure it on your own repositories rather than trust a single headline number.

Now scale that to a high volume agentic loop running thousands of turns per day: a CI bot triaging every failing build, an automated reviewer commenting on every pull request, a dev tool that drafts a patch on every flagged ticket. At ten thousand agentic turns a day, the per token price gap compounds into a difference of thousands of dollars a month for nearly the same throughput. When the work is high volume and individually low stakes, that ratio is the whole decision.

Where the Capability Gap Actually Shows

The cost story does not mean Grok 4.3 is a free lunch, but here we have to be careful about what is actually measured. The one knowledge work benchmark with a reported cross point is GDPval-AA, and on it Opus 4.8 leads clearly: 1890 ELO against Grok's roughly 1500 (for scale, Gemini 3.1 Pro sits at 1314). That is a real margin, not a rounding error. Beyond GDPval-AA the comparison thins out fast, because xAI did not publish SWE-Bench Pro, Terminal-Bench 2.1, or OSWorld-Verified numbers that line up against Anthropic's, and Anthropic's launch table did not include Grok. We are not going to invent those head to head figures. What we can say is that Opus 4.8's published suite (SWE-Bench Pro 69.2%, Terminal-Bench 2.1 74.6%, OSWorld-Verified 83.4%, Humanity's Last Exam 49.8% without tools and 57.9% with tools, Finance Agent v2 53.9%) sets a documented ceiling that Grok has not matched on the record.

In our own testing the gap widens on the hardest tasks: long multi step fixes that touch several files in the right order, and problems where the correct change depends on subtle semantics rather than pattern matching. The clearest failure mode we see from Grok 4.3 is numerical library semantics. On issues in numpy, sympy, and scikit learn, where the right fix hinges on broadcasting rules, dtype edge cases, or the precise contract of a function, Grok is more likely to produce a plausible looking patch that is subtly wrong. Opus 4.8 is more deliberate here, and it tends to produce minimal correct patches: surgical edits rather than sprawling rewrites that a reviewer then has to unwind. Read these as our observations, not published benchmarks.

The saving grace, again, is that in our runs Grok's misses are often quick misses. It does not usually grind for thousands of tokens before failing on these, which is exactly why its effective cost stays low even when its resolution rate is lower. You pay less per attempt and less per failure; you just close fewer of the genuinely hard ones.

Speed and Latency

The verified point here is simple: Grok 4.3 is built for speed and is cheaper, so for latency sensitive high volume work it has a structural edge. In our testing it is the faster of the two on a median agent task, on the order of 24s against roughly 31s for Opus 4.8, though treat those specific seconds as editorial estimates from our runs rather than published figures. For interactive use (a developer waiting on an inline suggestion or a chat turn) the difference is felt directly. For high throughput batch loops it matters even more, because lower per turn latency raises the ceiling on how many turns you can push through a fixed window of wall clock time and concurrency.

Opus 4.8 does have a lever of its own: its fast mode is a premium tier (priced at $10 per 1M input and $50 per 1M output) that runs the same model about 2.5x faster with no quality downgrade. It narrows the responsiveness gap, but it does so by paying up rather than by undercutting Grok on price. If raw responsiveness at low cost is the priority, Grok 4.3 is still the cheaper way to get there; if you want speed without leaving Opus 4.8's capability ceiling, fast mode is the path.

Multimodal

Both models handle text and images. The difference is video. Grok 4.3 adds native video input, and Opus 4.8 does not. If your workload reasons over video (analyzing recorded sessions, inspecting screen capture, processing camera or product footage) Grok is the only one of the two that does it natively, and it is the clear pick for that case. For text and image work the modality difference does not factor in.

Where Opus 4.8 Wins

• Published benchmark leadership: it carries a full launch suite (SWE-Bench Pro 69.2%, Terminal-Bench 2.1 74.6%, OSWorld-Verified 83.4%, Finance Agent v2 53.9%) and leads the one head to head point, GDPval-AA, at 1890 against Grok's roughly 1500.
• Reasoning under tools: Humanity's Last Exam at 49.8% without tools and 57.9% with tools shows it holds up when the problem is genuinely hard.
• Agentic coding and minimal correct patches: surgical edits that survive review rather than broad rewrites, and planning that holds across a long chain of tool calls.
• Capability ceiling: when an individual turn is high stakes, it is the safer default.
• Fast mode for latency without leaving the ceiling: a premium tier at $10 per 1M input and $50 per 1M output runs the same model about 2.5x faster with no quality downgrade.

Where Grok 4.3 Wins

• Price per token: input $1.25 and output $2.50, one quarter of Opus on input and one tenth on output, the cheapest of the two by a wide margin.
• 1M context at low cost: a full 1M token window at Grok's per token price makes large context agentic work far cheaper to run.
• Speed: built for low latency, the faster of the two in our testing, which compounds across high volume loops.
• Native video input that Opus 4.8 does not offer.
• Unit economics for high volume loops: at thousands of turns a day the per token price gap is the deciding factor.

When This Applies to Your Stack

The honest answer for most teams is that this is not an either or. The right pattern is to route by stakes. Send high volume, individually cheap agentic turns to Grok 4.3: CI triage, automated review passes, first draft patches, anything where a quick wrong answer is cheap to discard. Reserve Opus 4.8 for the high stakes turns where a wrong patch is expensive: the gnarly multi file fix, the change that touches a numerical core, the output that has to be correct the first time.

In practice that means putting a gateway in front of both models so routing is a config decision rather than a code change, caching stable system prompts to drive the per turn cost down further, and running an eval harness on your own repositories so you are measuring effective cost on your code, not on a public benchmark. A fallback path (escalate to Opus 4.8 when Grok 4.3 fails a turn or fails an eval) often captures most of the savings while protecting the hard cases.

This is the kind of routing, caching, and evaluation work Contra Collective builds for teams shipping agentic features. If you want help wiring a gateway, designing the eval harness on your own task distribution, or setting the escalation thresholds, we can take that on.

FAQ

Is Grok 4.3 as good as Opus 4.8? Near the frontier on general capability, but not equal where it is measured. The comparison is asymmetric: Anthropic published a full benchmark suite for Opus 4.8, while xAI did not publish SWE-Bench Pro, Terminal-Bench 2.1, or OSWorld-Verified numbers that compare directly. On the one head to head point, GDPval-AA, Opus 4.8 leads clearly (1890 against Grok's roughly 1500). In our own testing Opus also holds a higher ceiling on the hardest multi step tasks. Grok competes on price, speed, native video, and a 1M context window rather than on the benchmark sheet.

Which is cheaper? Grok 4.3, by a wide and verified margin on list pricing: input is $1.25 against Opus 4.8's $5 (one quarter) and output is $2.50 against $25 (one tenth). On effective cost per resolved task we estimate Grok stays favorable because it tends to fail fast, but treat that as our read from internal runs rather than a published figure.

Which is faster? Grok 4.3 is built for speed and is the faster of the two in our testing. Opus 4.8 offers a fast mode, a premium tier at $10 per 1M input and $50 per 1M output that runs the same model about 2.5x faster with no quality downgrade, which narrows the gap by paying up rather than by undercutting Grok on price.

Which one for video? Grok 4.3. It supports native video input; Opus 4.8 handles text and images only. If your workload reasons over video, Grok is the pick.

Can I route between them? Yes, and for high volume agentic work you usually should. Put a gateway in front of both, send cheap high volume turns to Grok 4.3, escalate high stakes turns to Opus 4.8, and use prompt caching and an eval harness to tune the split on your own tasks.