DeepSeek Reasoner vs DeepSeek v3.2
DeepSeek Reasoner (DeepSeek, 131,072-token context) versus DeepSeek v3.2 (DeepSeek, 163,840-token context). DeepSeek v3.2 is cheaper by 3% on a blended token mix. DeepSeek Reasoner uniquely supports structured output (json schema). DeepSeek v3.2 uniquely supports function calling. 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 — DeepSeek Reasoner vs DeepSeek v3.2
DeepSeek Reasoner and DeepSeek v3.2 are priced within 3% 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.
On capability surface area, the models diverge: DeepSeek Reasoner supports structured output (json schema) where the other does not; DeepSeek v3.2 supports function calling 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: deepseek-v3-2
provider: deepseek
fallback:
model: deepseek-reasoner
provider: deepseek
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek Reasoner | DeepSeek v3.2 | |
|---|---|---|
| Input price | $0.280/M | $0.280/M |
| Output price | $0.420/M | $0.400/M |
| Context window | 131,072 | 163,840 |
| Max output | 65,536 | 163,840 |
| Function calling | — | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Aug 6, 2026 | Aug 6, 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 | DeepSeek Reasoner | DeepSeek v3.2 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $109 /mo | $108 /mo | $1.20/mo |
| Mid-market 100K requests/day | $1,092 /mo | $1,080 /mo | $12.00/mo |
| Enterprise 1M requests/day | $10,920 /mo | $10,800 /mo | $120/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 agent calls tools or APIs — DeepSeek v3.2 supports function calling natively, the other model needs a parser shim.
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 DeepSeek Reasoner, switching to DeepSeek v3.2 means re-architecting that path (and vice versa).
- • Structured output (JSON schema)
- • Function calling
Capabilities both share (3)
- ✓ Streaming
- ✓ Prompt caching
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 65,536 on DeepSeek Reasoner vs 163,840 on DeepSeek v3.2. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- DeepSeek Reasoner has capabilities DeepSeek v3.2 lacks: Structured output (JSON schema). Switching to DeepSeek v3.2 means re-architecting any flow that depends on these.
- DeepSeek v3.2 has capabilities DeepSeek Reasoner lacks: Function calling. Worth wiring through the agent design before commit.
How to A/B test DeepSeek Reasoner 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 DeepSeek Reasoner 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 — DeepSeek Reasoner vs DeepSeek v3.2
Which is cheaper, DeepSeek Reasoner or DeepSeek v3.2? ▾
DeepSeek v3.2 is cheaper by roughly 3% on a blended input + output token mix. Input prices are $0.280/M for DeepSeek Reasoner versus $0.280/M for DeepSeek v3.2; output prices are $0.420/M versus $0.400/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 DeepSeek Reasoner versus DeepSeek v3.2? ▾
DeepSeek Reasoner supports up to 131,072 tokens of context. DeepSeek v3.2 supports up to 163,840 tokens. DeepSeek v3.2 has the larger window by a factor of 1.3x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do DeepSeek Reasoner and DeepSeek v3.2 both support tool calling? ▾
Only DeepSeek v3.2 supports native function calling. The other model can still be made to call tools through a structured-output workaround, but the reliability of that pattern is lower than native support.
Which model supports prompt caching for cost reduction? ▾
Both DeepSeek Reasoner 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.
How do I A/B test DeepSeek Reasoner 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.