Grok API Pricing (August 2026): Grok 4.6 vs. Grok 4.5 Rates and LLM Cost Tradeoffs

Grok 4.6 is the sensible xAI default when the newer model matters to your product or workflow; Grok 4.5 is the better cost-control choice when similar task performance is acceptable in your own evaluation. The pricing gap is meaningful but not extreme: at listed OneMux rates, Grok 4.6 costs $1.25 per million input tokens and $3.50 per million output tokens, versus $0.85 and $2.50 for Grok 4.5.

The practical decision is not simply “which model is cheaper?” It is whether your workload is input-heavy, output-heavy, latency-sensitive, or quality-sensitive. For most generation products, output tokens drive the larger part of the bill.

Grok 4.6 and Grok 4.5 API pricing at a glance

The following rates are the supplied OneMux catalogue prices. Token prices are stated per 1 million tokens.

On a balanced 1M-input, 1M-output workload, Grok 4.6 costs $1.40 more than Grok 4.5, or about 42% more. Put differently, Grok 4.5 costs about 29% less than Grok 4.6 for that token mix.

That difference grows with generated length. If a support assistant, marketing workflow, or coding agent routinely produces long outputs, model selection has a direct operating-cost effect.

The output-token math that should drive the decision

A useful formula is:

Request cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate)

Consider three common monthly token mixes. These examples isolate token charges and do not assume a particular product, response quality, or request volume.

For document analysis, retrieval-augmented answers, and internal search, input volume can be substantial because retrieved context and conversation history are passed to the model. For copy generation, support replies, code generation, and multi-step agents, output volume can dominate. Grok 4.6’s output rate is 40% higher than Grok 4.5’s, so reducing unnecessary response length can matter as much as changing models.

Use explicit output limits, return structured fields rather than verbose prose where appropriate, and avoid repeatedly sending unneeded chat history. Those are product and prompt decisions, not merely procurement decisions.

How Grok 4.6 compares with other listed LLM APIs

Grok 4.6 occupies a middle position in the supplied catalogue. Its $3.50 output rate is lower than GPT 5.6 Terra ($4.80), GPT 5.6 Sol ($9.60), and Claude Sonnet 5 ($5.50). It is higher than DeepSeek V4 Pro ($2.10) and Grok 4.5 ($2.50).

For founders and operators, that means Grok 4.6 is not the absolute lowest-price route for token-intensive work. It can be a reasonable candidate when it meets the product’s quality requirements and its pricing fits the workload. The appropriate comparison is task-specific: evaluate models on the prompts, languages, tools, formats, safety handling, and response lengths your users actually create.

For international buyers, normalize every vendor quote to the same unit: input tokens, output tokens, and the same expected token mix. A headline “per million tokens” number can conceal a much higher output charge. Also confirm the billing currency, payment process, and applicable taxes with the provider before budgeting.

A note on external pricing comparisons

BenchLM’s xAI pricing comparison states in its supplied summary that Grok 4.6 output tokens cost $6 per million compared with $30 per million for GPT-5.6 Sol in the comparison cited there. That is a useful directional observation about output-price differences, but it is not the same as the OneMux catalogue rates shown above. Pricing can differ by route, date, and billing context. Use the rate card attached to the API path you intend to buy from when calculating production spend.

When to choose Grok 4.6, Grok 4.5, or another model

Choose Grok 4.6 when you want the newer xAI model and your evaluation shows that its results are worth the additional $0.40 per million input tokens and $1.00 per million output tokens over Grok 4.5.

Choose Grok 4.5 when you need an xAI option with lower token charges, particularly for high-volume generation. It is the straightforward first comparison point for teams considering Grok 4.6 but watching gross margin.

Choose a lower-cost alternative such as DeepSeek V4 Pro when cost is the dominant constraint and the model passes your acceptance tests. Consider another model family when a workflow depends on capabilities, formats, or behavior that your evaluation finds more reliable elsewhere. Price alone cannot establish that a model is suitable for coding, customer support, legal review, or multilingual publishing.

A practical routing policy can be simple: send ordinary drafts and high-volume jobs to the lowest-cost model that meets quality thresholds, and reserve Grok 4.6 for prompts where it demonstrably improves an outcome you measure.

Implement Grok pricing controls without rebuilding your API layer

OneMux provides access to leading AI models through one OpenAI-compatible API. That is useful for teams that want to compare Grok 4.6, Grok 4.5, and alternatives without maintaining separate provider integrations. OneMux focuses on model access, routing, keys, spend visibility, credit top-ups, and lower-cost pay-as-you-go usage.

Start with the OneMux quickstart guide, then keep the model name configurable rather than hard-coding it in application logic. This makes it easier to run controlled tests and update a routing rule as pricing or requirements change.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_ONEMUX_API_KEY",
    base_url="YOUR_ONEMUX_BASE_URL"
)

response = client.chat.completions.create(
    model="grok-4-6",
    messages=[
        {"role": "user", "content": "Draft a concise support reply."}
    ],
    max_tokens=350
)

print(response.choices[0].message.content)

Before launch, set a token budget per workflow, record input and output tokens by model, and review spend by feature rather than only at account level. Consult the OneMux pricing page and API documentation for current account and integration details.

Conclusion

Grok 4.6’s listed OneMux rate of $1.25 per million input tokens and $3.50 per million output tokens makes it a competitively priced option for teams that want the newer xAI model. Grok 4.5 remains the stronger price choice within the pair, especially for output-heavy workloads. The right production decision is to test both on representative prompts, calculate costs using your real input-to-output ratio, and route each workflow to the model that clears its quality bar at an acceptable unit cost.

Sources

• BenchLM: xAI API pricing comparison
• OneMux Grok 4.6 model page
• OneMux Grok 4.5 model page
• OneMux models catalogue