Grok 4.5 vs Claude Opus 4.8: Which one is the Best

When xAI shipped Grok 4.5 on July 8, 2026, Elon Musk framed it as “an Opus-class model, but faster, more token-efficient and lower cost.” That is a specific claim against Claude Opus 4.8, Anthropic’s daily-work coding model.

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This article checks that claim using xAI’s published numbers from its announcement, Anthropic’s published pricing, and the practical tradeoffs that matter when you wire either model into real coding workflows.

Short version: Grok 4.5 is broadly “Opus-class,” cheaper on tokens, and likely faster in many agent loops. But Opus 4.8 still wins two important repo-level benchmarks, and Grok 4.5 does not yet have fully independent benchmark coverage.

The scorecard

xAI published four coding benchmarks. Here is the direct comparison:

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)

That is a 2-2 split.

For implementation planning, treat this as:

• Grok 4.5: strong enough to test as a lower-cost replacement in coding agents.
• Opus 4.8: still ahead on some harder repo-level tasks.
• Neither model is a clear universal winner based on these four numbers alone.

Also note the frontier context: Claude Fable 5 (max) tops all four of these charts, 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. For the frontier comparison, see Fable 5 vs Opus 4.8.

Benchmark provenance: verify before migrating

These are not fully independent test results.

xAI says competitor figures come from each developer’s published system cards or benchmark leaderboards. DeepSWE evals were created by Datacurve, and runs used each provider’s harness.

That is better than opaque self-reporting, but it still means differences in harnesses, scaffolds, prompts, retries, and tool use can affect the comparison.

Before moving production traffic, run both models against your own workload:

1. Pick 5–20 real prompts from your coding agent or internal tools.
2. Run both models with the same system prompt and tool schema.
3. Capture:
   • task success
   • output quality
   • latency
   • input tokens
   • output tokens
   • total cost
4. Review failures manually.
5. Only migrate the workload if the cheaper model preserves the success rate you need.

Our Grok 4.5 benchmarks deep dive tracks the independent-benchmark situation.

Price: Grok wins on list price

Sticker prices per million tokens:

Token type	Grok 4.5	Opus 4.8
Input	$2.00	$5.00
Output	$6.00	$25.00

That makes Grok 4.5:

• 40% of Opus 4.8’s input price
• 24% of Opus 4.8’s output price

See the Anthropic-side details in our Opus 4.8 pricing breakdown.

Price per completed task: output length matters

The bigger difference is output 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 output tokens on the same benchmark.

Using the published output prices:

Grok 4.5:
15,954 tokens × $6 / 1,000,000 ≈ $0.10 output cost per task

Opus 4.8 (max):
67,020 tokens × $25 / 1,000,000 ≈ $1.68 output cost per task

That is roughly 17x cheaper on output per resolved task, using vendor-reported token counts.

Do not overfit to the exact multiplier. It depends on:

• one benchmark
• vendor-reported token usage
• Opus 4.8 running in “max” mode
• your prompt style
• your required answer format

But the implementation lesson is clear: if Grok 4.5 produces shorter successful answers on your workload, the cost gap can be much larger than the price table suggests.

We cover fuller cost scenarios in Grok 4.5 pricing explained.

The caveat cuts the other way: Opus spends more tokens partly because “max” mode reasons at length, and that reasoning may be why it wins SWE Bench Pro by 4.5 points. Sometimes you are paying for additional thinking, not just verbosity.

Speed: shorter answers compound in agent loops

xAI reports Grok 4.5 at about 80 tokens per second, which it calls “fast-model speeds.”

For single prompts, that helps. For agents, it matters more because latency compounds across steps:

task → inspect files → plan patch → edit → run tests → fix error → summarize

If each step emits fewer tokens and those tokens stream faster, total wall-clock time can drop noticeably.

Anthropic does not publish an equivalent TPS figure for Opus 4.8, and real-world speed varies by load, region, and account tier. Measure it on your own traffic.

At minimum, log:

{
  "model": "grok-4.5-or-opus-4.8",
  "started_at": "timestamp",
  "completed_at": "timestamp",
  "input_tokens": 12345,
  "output_tokens": 6789,
  "task_success": true
}

Then compare:

latency_seconds = completed_at - started_at
cost = input_tokens * input_price + output_tokens * output_price

Where Opus 4.8 still has the edge

Grok 4.5 is cheaper, but price is not the only production constraint.

Opus 4.8 remains stronger in three practical areas:

• Hard repo-level coding: SWE Bench Pro goes to Opus by 4.5 points. DeepSWE 1.1 goes to Opus by 6 points.
• Ecosystem maturity: Opus 4.8 fits into Claude Code, established tool-use patterns, and existing production workflows. Grok 4.5 launched recently, so migration risk is real.
• Predictability: Opus behavior under long contexts, tool use, refusal patterns, and failure modes is better documented, including our Claude Opus 4.8 API guide.

How to decide which model to use

Use Grok 4.5 if

Choose Grok 4.5 when:

• you run high-volume coding-agent workloads
• output-token cost is a major budget line
• your tasks tolerate week-one model risk
• you can validate quality with your own test suite
• you want to exploit lower output pricing and shorter answers

Launch pricing plus the current free windows make it cheap to test.

Stay on Opus 4.8 if

Keep Opus 4.8 when:

• you are already deep in the Anthropic ecosystem
• you need stronger published scores on hard repo-level benchmarks
• your workflows depend on mature Claude tooling
• production stability matters more than token savings
• you cannot absorb early model behavior changes

Build a simple A/B harness

Both APIs use OpenAI-compatible shapes for basic chat completions, so you can test them with a small harness by swapping:

• base_url
• API key
• model ID

Example structure:

const cases = [
  {
    name: "fix failing unit test",
    messages: [
      {
        role: "system",
        content: "You are a senior software engineer. Return concise, actionable patches."
      },
      {
        role: "user",
        content: "Here is the failing test output and relevant code..."
      }
    ]
  }
];

const models = [
  {
    name: "grok-4.5",
    baseUrl: process.env.GROK_BASE_URL,
    apiKey: process.env.GROK_API_KEY,
    model: "grok-4.5"
  },
  {
    name: "opus-4.8",
    baseUrl: process.env.ANTHROPIC_COMPAT_BASE_URL,
    apiKey: process.env.ANTHROPIC_API_KEY,
    model: "claude-opus-4.8"
  }
];

async function runCase(clientConfig, testCase) {
  const started = Date.now();

  const res = await fetch(`${clientConfig.baseUrl}/chat/completions`, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${clientConfig.apiKey}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: clientConfig.model,
      messages: testCase.messages
    })
  });

  const data = await res.json();
  const completed = Date.now();

  return {
    model: clientConfig.name,
    case: testCase.name,
    latency_ms: completed - started,
    output: data.choices?.[0]?.message?.content,
    usage: data.usage
  };
}

for (const testCase of cases) {
  for (const model of models) {
    const result = await runCase(model, testCase);
    console.log(JSON.stringify(result, null, 2));
  }
}

For each result, compare:

• Did it solve the task?
• Was the answer usable without extra prompting?
• Did it call tools correctly?
• How many output tokens did it use?
• How long did it take?
• What was the estimated cost?

Pay special attention to output_tokens. If Grok’s answers are not shorter on your prompts, the cost advantage may be smaller than the benchmark math suggests.

You can also run the comparison manually in Apidog: save one request per model, send the same prompts, and compare response quality, latency, and usage side by side. Download Apidog free to test both APIs against your own traffic.

Setup guides for both sides:

• How to use the Grok 4.5 API
• How to use the Claude Opus 4.8 API

FAQ

Is Grok 4.5 better than Claude Opus 4.8?

Not universally. They split xAI’s published benchmarks 2-2. Grok 4.5 is cheaper and more token-efficient. Opus 4.8 wins two harder repo-level evaluations.

How much cheaper is Grok 4.5 than Opus 4.8?

List pricing is $2 vs $5 per million input tokens and $6 vs $25 per million output tokens. Per resolved coding task, vendor-reported token counts suggest the effective output-cost gap can be much larger.

Are there independent benchmarks for Grok 4.5?

Not yet. Published figures mix xAI’s runs, Datacurve evals, and other vendors’ system cards or leaderboards. Treat the numbers as useful but not final.

Can I test both models with the same code?

Mostly, yes. Both expose OpenAI-compatible chat-completion shapes for basic usage, so swapping base_url, API key, and model ID covers the basics. Tool calling and structured-output behavior can differ, so test those paths explicitly before switching production workloads.