Claude 3 Opus (2024-02-29) vs GPT-4
Claude 3 Opus (2024-02-29) (Anthropic, 200,000-token context) versus GPT-4 (Azure OpenAI, 8,192-token context). Claude 3 Opus (2024-02-29) is cheaper by 0% on a blended token mix. Claude 3 Opus (2024-02-29) uniquely supports vision input and structured output (json schema). Across 1 public benchmark we tracked, Claude 3 Opus (2024-02-29) wins 0 and GPT-4 wins 1. 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 Opus (2024-02-29) vs GPT-4
Claude 3 Opus (2024-02-29) and GPT-4 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 Opus (2024-02-29) ships a 200,000-token context window, 24.4x larger than GPT-4's 8,192 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 8,192 tokens, the extra context on Claude 3 Opus (2024-02-29) is insurance you may never use — and GPT-4 may win on other axes.
On capability surface area, the models diverge: Claude 3 Opus (2024-02-29) supports vision input where the other does not; Claude 3 Opus (2024-02-29) supports structured output (json schema) where the other does not; Claude 3 Opus (2024-02-29) supports prompt caching 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: claude-3-opus-20240229
provider: anthropic
fallback:
model: gpt-4
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude 3 Opus (2024-02-29) | GPT-4 | |
|---|---|---|
| Input price | $15.00/M | $30.00/M |
| Output price | $75.00/M | $60.00/M |
| Context window | 200,000 | 8,192 |
| Max output | 4,096 | 4,096 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | — | — |
| Prompt caching | ✓ | — |
| Structured output | ✓ | — |
| Pricing verified | Aug 6, 2026 | Oct 22, 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 Opus (2024-02-29) | GPT-4 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $9,000 /mo | $12,600 /mo | $3,600/mo |
| Mid-market 100K requests/day | $90,000 /mo | $126,000 /mo | $36,000/mo |
| Enterprise 1M requests/day | $900,000 /mo | $1,260,000 /mo | $360,000/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 workload needs long context — Claude 3 Opus (2024-02-29) fits 200,000 tokens versus the other model's 8,192, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Claude 3 Opus (2024-02-29) accepts image input natively, the other doesn't.
You re-send the same large system prompt across requests — Claude 3 Opus (2024-02-29) supports prompt caching, cutting input cost on repeat hits.
On arena-elo, GPT-4 scores 40.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
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 Opus (2024-02-29), switching to GPT-4 means re-architecting that path (and vice versa).
- • Vision input
- • Structured output (JSON schema)
- • Prompt caching
Capabilities both share (2)
- ✓ Function calling
- ✓ Streaming
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | Claude 3 Opus (2024-02-29) | GPT-4 | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1248.0 | 1288.0 | GPT-4 | +40.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 96% when moving from Claude 3 Opus (2024-02-29) (200,000) to GPT-4 (8,192). Re-check any prompt that relies on cramming long history or documents.
- Claude 3 Opus (2024-02-29) has capabilities GPT-4 lacks: Vision input, Structured output (JSON schema), Prompt caching. Switching to GPT-4 means re-architecting any flow that depends on these.
- Provider changes from Anthropic to Azure OpenAI. 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 Opus (2024-02-29) vs GPT-4 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 Opus (2024-02-29) primary, mirror 20% of traffic to GPT-4 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 Opus (2024-02-29) vs GPT-4
What is the context window of Claude 3 Opus (2024-02-29) versus GPT-4? ▾
Claude 3 Opus (2024-02-29) supports up to 200,000 tokens of context. GPT-4 supports up to 8,192 tokens. Claude 3 Opus (2024-02-29) has the larger window by a factor of 24.4x, 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 Opus (2024-02-29) and GPT-4 both support tool calling? ▾
Yes — both Claude 3 Opus (2024-02-29) and GPT-4 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 Opus (2024-02-29) and GPT-4 process images? ▾
Claude 3 Opus (2024-02-29) accepts native image input. GPT-4 does not — you would need to route image-heavy workloads through Claude 3 Opus (2024-02-29) or add a separate vision model in front of GPT-4.
Which model supports prompt caching for cost reduction? ▾
Claude 3 Opus (2024-02-29) supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Claude 3 Opus (2024-02-29) gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Claude 3 Opus (2024-02-29) over GPT-4? ▾
Your workload needs long context — Claude 3 Opus (2024-02-29) fits 200,000 tokens versus the other model's 8,192, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Claude 3 Opus (2024-02-29) accepts image input natively, the other doesn't. You re-send the same large system prompt across requests — Claude 3 Opus (2024-02-29) supports prompt caching, cutting input cost on repeat hits.
When should I choose GPT-4 over Claude 3 Opus (2024-02-29)? ▾
On arena-elo, GPT-4 scores 40.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test Claude 3 Opus (2024-02-29) against GPT-4 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.