Claude 3.5 Sonnet (2024-06-20) vs Grok 3
Claude 3.5 Sonnet (2024-06-20) (Anthropic, 200,000-token context) versus Grok 3 (Azure AI Foundry, 131,072-token context). Claude 3.5 Sonnet (2024-06-20) is cheaper by 0% on a blended token mix. Claude 3.5 Sonnet (2024-06-20) uniquely supports vision input and pdf input. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Claude 3.5 Sonnet (2024-06-20) vs Grok 3
Claude 3.5 Sonnet (2024-06-20) and Grok 3 are priced within 0% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
Claude 3.5 Sonnet (2024-06-20) ships a 200,000-token context window, 1.5x larger than Grok 3's 131,072 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 131,072 tokens, the extra context on Claude 3.5 Sonnet (2024-06-20) is insurance you may never use — and Grok 3 may win on other axes.
On capability surface area, the models diverge: Claude 3.5 Sonnet (2024-06-20) supports vision input where the other does not; Claude 3.5 Sonnet (2024-06-20) supports pdf input where the other does not; Claude 3.5 Sonnet (2024-06-20) supports structured output (json schema) where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: grok-3
provider: azure-ai-foundry
fallback:
model: claude-3-5-sonnet-20240620
provider: anthropic
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude 3.5 Sonnet (2024-06-20) | Grok 3 | |
|---|---|---|
| Input price | $3.00/M | $3.00/M |
| Output price | $15.00/M | $15.00/M |
| Context window | 200,000 | 131,072 |
| Max output | 8,192 | 131,072 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | — | — |
| Prompt caching | ✓ | — |
| Structured output | ✓ | — |
| Pricing verified | May 7, 2026 | May 19, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Claude 3.5 Sonnet (2024-06-20) | Grok 3 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,800 /mo | $1,800 /mo | — |
| Mid-market 100K requests/day | $18,000 /mo | $18,000 /mo | — |
| Enterprise 1M requests/day | $180,000 /mo | $180,000 /mo | — |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your inputs include screenshots, diagrams, or product photos — Claude 3.5 Sonnet (2024-06-20) accepts image input natively, the other doesn't.
You re-send the same large system prompt across requests — Claude 3.5 Sonnet (2024-06-20) supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Claude 3.5 Sonnet (2024-06-20), switching to Grok 3 means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Structured output (JSON schema)
- • Prompt caching
Capabilities both share (2)
- ✓ Function calling
- ✓ Streaming
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 34% when moving from Claude 3.5 Sonnet (2024-06-20) (200,000) to Grok 3 (131,072). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on Claude 3.5 Sonnet (2024-06-20) vs 131,072 on Grok 3. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Claude 3.5 Sonnet (2024-06-20) has capabilities Grok 3 lacks: Vision input, PDF input, Structured output (JSON schema), Prompt caching. Switching to Grok 3 means re-architecting any flow that depends on these.
- Provider changes from Anthropic to Azure AI Foundry. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Claude 3.5 Sonnet (2024-06-20) vs Grok 3 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Claude 3.5 Sonnet (2024-06-20) primary, mirror 20% of traffic to Grok 3 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Claude 3.5 Sonnet (2024-06-20) vs Grok 3
What is the context window of Claude 3.5 Sonnet (2024-06-20) versus Grok 3? ▾
Claude 3.5 Sonnet (2024-06-20) supports up to 200,000 tokens of context. Grok 3 supports up to 131,072 tokens. Claude 3.5 Sonnet (2024-06-20) has the larger window by a factor of 1.5x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Claude 3.5 Sonnet (2024-06-20) and Grok 3 both support tool calling? ▾
Yes — both Claude 3.5 Sonnet (2024-06-20) and Grok 3 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Claude 3.5 Sonnet (2024-06-20) and Grok 3 process images? ▾
Claude 3.5 Sonnet (2024-06-20) accepts native image input. Grok 3 does not — you would need to route image-heavy workloads through Claude 3.5 Sonnet (2024-06-20) or add a separate vision model in front of Grok 3.
Which model supports prompt caching for cost reduction? ▾
Claude 3.5 Sonnet (2024-06-20) supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Claude 3.5 Sonnet (2024-06-20) gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Claude 3.5 Sonnet (2024-06-20) over Grok 3? ▾
Your inputs include screenshots, diagrams, or product photos — Claude 3.5 Sonnet (2024-06-20) accepts image input natively, the other doesn't. You re-send the same large system prompt across requests — Claude 3.5 Sonnet (2024-06-20) supports prompt caching, cutting input cost on repeat hits.
When should I choose Grok 3 over Claude 3.5 Sonnet (2024-06-20)? ▾
On the data this page surfaces, Grok 3 is the right pick when Claude 3.5 Sonnet (2024-06-20)'s lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test Claude 3.5 Sonnet (2024-06-20) against Grok 3 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.