Claude Opus 4.6 vs DeepSeek v3.2
Claude Opus 4.6 (Anthropic, 1,000,000-token context) versus DeepSeek v3.2 (Azure AI Foundry, 163,840-token context). DeepSeek v3.2 is cheaper by 92% on a blended token mix. Claude Opus 4.6 uniquely supports vision input and pdf input. Across 2 public benchmarks we tracked, Claude Opus 4.6 wins 2 and DeepSeek v3.2 wins 0. 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 Opus 4.6 vs DeepSeek v3.2
Claude Opus 4.6 and DeepSeek v3.2 target overlapping workloads but differ sharply on economics. DeepSeek v3.2 runs roughly 92% cheaper on a blended input-plus-output token mix, which translates to approximately $27,252 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Claude Opus 4.6 ships a 1,000,000-token context window, 6.1x larger than DeepSeek v3.2's 163,840 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 163,840 tokens, the extra context on Claude Opus 4.6 is insurance you may never use — and DeepSeek v3.2 may win on other axes.
On capability surface area, the models diverge: Claude Opus 4.6 supports vision input where the other does not; Claude Opus 4.6 supports pdf input where the other does not; Claude Opus 4.6 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.
Across 2 public benchmarks, Claude Opus 4.6 leads on 2 and DeepSeek v3.2 leads on 0. The widest gap is on swe-bench-verified, where Claude Opus 4.6 scores 28.9 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
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: deepseek-v3-2
provider: azure-ai-foundry
fallback:
model: claude-opus-4-6
provider: anthropic
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude Opus 4.6 | DeepSeek v3.2 | |
|---|---|---|
| Input price | $5.00/M | $0.580/M |
| Output price | $25.00/M | $1.68/M |
| Context window | 1,000,000 | 163,840 |
| Max output | 128,000 | 163,840 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Jun 2, 2026 | Jun 2, 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 Opus 4.6 | DeepSeek v3.2 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $3,000 /mo | $275 /mo | $2,725/mo |
| Mid-market 100K requests/day | $30,000 /mo | $2,748 /mo | $27,252/mo |
| Enterprise 1M requests/day | $300,000 /mo | $27,480 /mo | $272,520/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.
You're cost-sensitive at scale — DeepSeek v3.2 runs ~92% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Claude Opus 4.6 fits 1,000,000 tokens versus the other model's 163,840, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Claude Opus 4.6 accepts image input natively, the other doesn't.
On swe-bench-verified, Claude Opus 4.6 scores 28.9 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 Opus 4.6, switching to DeepSeek v3.2 means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Structured output (JSON schema)
Capabilities both share (4)
- ✓ Function calling
- ✓ Streaming
- ✓ Prompt caching
- ✓ Native reasoning mode
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 Opus 4.6 | DeepSeek v3.2 | Winner | Δ |
|---|---|---|---|---|
| gpqa-diamond | 91.3 | 67.9 | Claude Opus 4.6 | +23.4 |
| swe-bench-verified | 81.4 | 52.5 | Claude Opus 4.6 | +28.9 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 84% when moving from Claude Opus 4.6 (1,000,000) to DeepSeek v3.2 (163,840). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 128,000 on Claude Opus 4.6 vs 163,840 on DeepSeek v3.2. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Claude Opus 4.6 has capabilities DeepSeek v3.2 lacks: Vision input, PDF input, Structured output (JSON schema). Switching to DeepSeek v3.2 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 Opus 4.6 vs DeepSeek v3.2 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 Opus 4.6 primary, mirror 20% of traffic to DeepSeek v3.2 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 Opus 4.6 vs DeepSeek v3.2
Which is cheaper, Claude Opus 4.6 or DeepSeek v3.2? ▾
DeepSeek v3.2 is cheaper by roughly 92% on a blended input + output token mix. Input prices are $5.00/M for Claude Opus 4.6 versus $0.580/M for DeepSeek v3.2; output prices are $25.00/M versus $1.68/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Claude Opus 4.6 versus DeepSeek v3.2? ▾
Claude Opus 4.6 supports up to 1,000,000 tokens of context. DeepSeek v3.2 supports up to 163,840 tokens. Claude Opus 4.6 has the larger window by a factor of 6.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Claude Opus 4.6 and DeepSeek v3.2 both support tool calling? ▾
Yes — both Claude Opus 4.6 and DeepSeek v3.2 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 Opus 4.6 and DeepSeek v3.2 process images? ▾
Claude Opus 4.6 accepts native image input. DeepSeek v3.2 does not — you would need to route image-heavy workloads through Claude Opus 4.6 or add a separate vision model in front of DeepSeek v3.2.
Which model supports prompt caching for cost reduction? ▾
Both Claude Opus 4.6 and DeepSeek v3.2 support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose Claude Opus 4.6 over DeepSeek v3.2? ▾
Your workload needs long context — Claude Opus 4.6 fits 1,000,000 tokens versus the other model's 163,840, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Claude Opus 4.6 accepts image input natively, the other doesn't. On swe-bench-verified, Claude Opus 4.6 scores 28.9 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose DeepSeek v3.2 over Claude Opus 4.6? ▾
You're cost-sensitive at scale — DeepSeek v3.2 runs ~92% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
How do I A/B test Claude Opus 4.6 against DeepSeek v3.2 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.