Two model evaluation paths feeding a shared workload, cost and integration decision

Comparison

Grok 4.7 vs Claude Opus 5: Benchmarks, Cost per Task, Coding Fit

Q: Are the reasoning controls interchangeable?

No. Opus 5 uses adaptive thinking steered by effort; Grok 4.7 uses four named reasoning_effort levels. Run the first pass at each default and record actual reasoning-token use.

Jerry

CGO

September 18, 2026

Updated on September 22, 2026

12 min read

Claude Opus 5 scores higher on independent benchmarks; Grok 4.7 lists at less than half the token price and cost about a third less to run the same test suite. On the Artificial Analysis Intelligence Index, Opus 5 scores 51 and Grok 4.7 scores 46, with the widest gap on Terminal-Bench 4.0. Running that index cost $7,275 on Opus 5 and $4,967 on Grok 4.7. Both models are live on EvoLink.

That is the shape of the decision: a measured quality gap against a measured cost gap, and the answer depends on the workload. This guide gives the numbers, the specifications, the cost-per-task method, and a workload-by-workload reading. It does not report an EvoLink benchmark.

Why this pairing

Before launch, Musk placed the intended Grok 4.7 level roughly around Opus 5.0. Anthropic positions Opus 5 for complex agentic coding and enterprise work; xAI's release notes position Grok 4.7 for coding, agents and knowledge work. The task overlap is real, and now that both models are testable the claim can be checked rather than debated. The release history is in the Grok 4.7 release record.

Specifications and rates

Decision input	Claude Opus 5	Grok 4.7
Provider model ID	`claude-opus-5`	`grok-4.7`
Released	July 24, 2026	September 21, 2026
Context window	1,000,000 tokens	500,000 tokens
Max output	128,000 tokens	Not published by xAI; EvoLink shows its route limit on the product page
Modalities	Text and images to text	Text and images to text
Reasoning control	Adaptive thinking, on by default; `effort` defaults to `high`	`reasoning_effort`: `low`, `medium`, `high` (default), `xhigh`
Provider list rate per 1M tokens	$5 input, $25 output; cache read $0.50; 5-minute cache write $6.25	$2 input, $6 output; cached input $0.50
Long-context rule	Not stated on the model overview; check Anthropic's pricing page	Prompt ≥ 200,000 tokens: every token billed at 2× ($4 / $1 / $12)
Faster variant	Fast mode (research preview, priced separately)	Grok 4.7 Fast: 2× speed, 2× price; Cursor and Grok Build only, not on the public API or EvoLink's route
Server-side tools	Anthropic tool use and hosted tools	xAI web search, X search, code execution, attachment and collections search, metered separately
EvoLink route	Live	Live since September 22, 2026

Opus 5 values come from Anthropic's model overview; Grok 4.7 values from xAI's model page and pricing page. Both checked September 22, 2026. EvoLink's current rates are on the Grok 4.7 and Claude Opus 5 product pages.

Two rows shape everything below. Opus 5's context is twice as large, which decides any single request that needs more than 500,000 tokens. And Grok 4.7's long-context rule doubles the rate for the entire request once the prompt reaches 200,000 tokens, which narrows the price gap for long-context work: at that tier Grok's $4 input and $12 output sit against Opus's $5 and $25.

Benchmarks: what third-party tests show

Artificial Analysis runs Grok 4.7 at xhigh and Claude Opus 5 at adaptive reasoning, max effort. Results as of September 22, 2026:

Measure	Claude Opus 5	Grok 4.7 (xhigh)
Intelligence Index v4.3.2	51	46
Terminal-Bench 4.0	49%	26%
Humanity's Last Exam	55%	43%
AutomationBench-AA	57%	66%
Output tokens to run the index	140M	240M
Cost to run the index	$7,275	$4,967
Output speed	56 tokens/s	39 tokens/s
Time to first token	49.25 s	0.85 s

Four readings. Opus 5 leads on the index and on the two hardest measures, with Terminal-Bench, the closest proxy for autonomous agent work, showing the widest gap. Grok 4.7 wins AutomationBench, a workplace-automation task set, by nine points. Grok 4.7 wrote 70% more output tokens to complete the same suite, yet the suite still cost a third less because its output rate is about a quarter of Opus's. And the latency profile is opposite: Opus 5 at max effort takes about 49 seconds before the first token, Grok 4.7 under a second, which matters for interactive use.

xAI's own numbers against Grok 4.6 are in the 4.7 vs 4.6 guide. They use a different harness and are not comparable with this table.

Cost per task, not cost per token

At list rates, Grok 4.7 input is 2.5× cheaper and output about 4.2× cheaper than Opus 5. The Artificial Analysis suite shows how much of that survives contact with a real workload: Grok 4.7 needed 240 million output tokens where Opus 5 needed 140 million, so the realised saving on the suite was about 32%, not 76%. That is the pattern to expect. A model with a lower rate can spend more tokens, and sometimes more attempts, to reach the same accepted result.

API cost per accepted task = all billed evaluation charges / accepted tasks

Use actual billed usage from the channel you run on. Keep input, output, cache and tool categories visible. If there are no accepted outcomes, report the failed evaluation instead of a zero.

Model choice compares accepted outcomes, all attempt costs and migration effort before expanding use of a model

An illustrative calculation, not a Grok or Claude measurement. Suppose Opus spends $40 across a task batch and accepts 80 outcomes: $0.50 each. Suppose Grok, at its lower rates but higher token use, spends $27 and accepts 72: $0.375 each. Grok is cheaper per accepted task but leaves eight more failures. If a failure costs a human retry at $2, Opus becomes $40 + $40 and Grok $27 + $56, and the order flips: $0.80 for Opus against $0.83 for Grok per delivered task. What a failure costs you decides the answer.

Then add switching work. If Grok saves $0.125 per accepted task and adapting the workflow costs $500, break-even is 4,000 accepted tasks. For a high-volume product that is days; for a low-volume internal tool it may never arrive.

Cache behaviour is worth measuring on both. Opus 5 bills a cache write at $6.25 per million for the 5-minute cache and then reads at $0.50; xAI bills cached input at $0.50 with no separate write charge on its published table. The effective input rate on each side depends on your hit ratio.

Which model for which workload

Workload	Reading from the evidence	What to check on your own tasks
Long-running coding agents	Opus 5 leads Terminal-Bench by 23 points; strongest case for Opus	Accepted-patch rate and cost per accepted task at each model's default effort
Bounded, high-volume tasks (extraction, classification, drafting)	Grok 4.7's rate advantage is largest where token use is predictable	Schema validity and per-task cost at `medium` or `high` effort
Interactive tools with latency budgets	Grok 4.7 returns a first token in under a second in AA's test; Opus 5 at max effort takes about 49 s	End-to-end completion time at the effort your product needs
Workplace automation flows	Grok 4.7 leads AutomationBench-AA by nine points	Whether the benchmark's task shape matches yours
Single requests above 500,000 tokens	Only Opus 5 can take the request	A chunking strategy that passes the same acceptance test
Requests between 200,000 and 500,000 tokens	Grok's long-context tier halves its price advantage	Compare the tiered Grok rate, not the headline rate

These are readings of third-party results, not guarantees. Both models document text and image input, function calling and structured outputs, so every category can be tested on both.

Run a fair evaluation

Hold task, tools, context and acceptance rules steady in the first pass. Record the configuration actually sent to each model and freeze tool responses where possible. The reasoning controls are the main trap: Opus 5 uses adaptive thinking steered by effort, Grok 4.7 uses four named reasoning_effort levels. Run the first pass at each model's default and record the reasoning and output tokens consumed. A second pass can tune each model within a fixed engineering budget; report untuned and tuned results separately.

Score the outcome of the task, not the number of tool calls. For coding agents, separate planning from implementation: a model may explain a strong plan and leave the patch incomplete. Use the same evaluator for both outputs, spot-check with a human or an executable validator, and hide model labels during subjective review.

What the gateway simplifies, and what you still adapt

EvoLink exposes both models through the same account, key and OpenAI-compatible endpoints, so the model ID becomes a configuration value. It does not make model-specific features portable.

Integration area	Work to inventory	Evidence the adapter is ready
Messages and system instructions	Roles, content blocks and retained constraints	Representative conversations preserve intended behaviour
Reasoning controls	Map `effort` and adaptive thinking to `reasoning_effort` per workload; do not assume equivalence	Recorded reasoning-token use per setting on both
Tools	Definitions, authorization and result format; Grok server tools vs application-side tools	Valid arguments, safe actions and recoverable errors
Structured results	Required fields and downstream validators	Accepted outputs pass the same application checks
Streaming	Partial events, interruption and completion handling	UI and backend handle every terminal outcome
Cost reporting	Usage fields, cache fields and task-attempt relationships	Totals reconcile with the actual bill on each route
Context budget	Requests near 200K on Grok, or above 500K at all	A compaction or chunking rule tested against the acceptance set

A shared API gateway connects model-specific adapters to application validators and state-aware recovery

When keeping both models is the right answer

The evidence supports a split more than a wholesale switch: route bounded, high-volume work to Grok 4.7 where it passes, and keep Opus 5 for long-running agent tasks and for requests that need more than 500,000 tokens. Define the input category, the acceptance rule and the escalation condition before assigning traffic.

For fallback, make the application responsible for state. If a tool has already written a file or performed an external action, the second model needs the updated state and a clear continuation rule. This is a rollout design for your application, not a promise that EvoLink automatically classifies workloads or transfers state between models.

A practical first evaluation on EvoLink

Choose one expensive or unreliable Opus 5 workload. Save a representative task set, current acceptance results and billed usage. Send the same set to grok-4.7 on the same EvoLink key at the default effort, and compare accepted outcomes and billed usage side by side. The Claude Opus 5 page and the Grok 4.7 page show each route's current rates and configuration.

If Grok 4.7 does not produce a meaningful gain, keep the existing workflow. If it wins one category, expand that category gradually with a tested recovery path. Teams already on Grok should use the Grok 4.7 vs 4.6 upgrade guide instead.

FAQ

Is Grok 4.7 as good as Claude Opus 5?

Not on independent benchmarks. Artificial Analysis scores Opus 5 at 51 and Grok 4.7 at 46 on its Intelligence Index, with Opus 5 ahead on Terminal-Bench 4.0 (49% vs 26%) and Humanity's Last Exam. Grok 4.7 leads AutomationBench-AA (66% vs 57%).

Which is better for coding, Grok 4.7 or Opus 5?

For long-running agentic coding, the Terminal-Bench gap favours Opus 5. For bounded coding tasks with predictable output, Grok 4.7's lower rate can win on cost per accepted task. Test your own repository tasks at each model's default effort.

How much cheaper is Grok 4.7 than Opus 5?

At list rates, 2.5× on input and about 4.2× on output. On Artificial Analysis's full suite the realised saving was about 32%, because Grok 4.7 used 70% more output tokens. EvoLink's current rates for both are on their product pages.

Which has the larger context window?

Opus 5, at 1,000,000 tokens versus 500,000. Opus 5 also documents a 128,000-token maximum output.

Which is faster?

In Artificial Analysis's test, Grok 4.7 returned its first token in 0.85 seconds against 49 seconds for Opus 5 at max effort, while Opus 5 streamed output faster once started (56 vs 39 tokens per second).

Are the reasoning controls interchangeable?

No. Opus 5 uses adaptive thinking steered by effort; Grok 4.7 uses four named reasoning_effort levels. Run the first pass at each default and record actual reasoning-token use.

Can both models be evaluated through EvoLink now?

Yes. Both are live routes on the same account and endpoints, with the model IDs claude-opus-5 and grok-4.7.

When is it worth keeping both models?

When each has a measurable role: Grok 4.7 for bounded, high-volume work where it passes, Opus 5 for long-running agents and requests above 500,000 tokens.

Sources

All Posts

#Grok 4.7 #Claude Opus 5 #Model Comparison #Cost Efficiency