DeepSeek R1 vs DeepSeek V3
DeepSeek R1 (Azure AI Foundry, 128,000-token context) versus DeepSeek V3 (Azure AI Foundry, 128,000-token context). DeepSeek V3 is cheaper by 16% on a blended token mix. DeepSeek R1 uniquely supports native reasoning mode. Across 8 public benchmarks we tracked, DeepSeek R1 wins 8 and DeepSeek V3 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 — DeepSeek R1 vs DeepSeek V3
DeepSeek R1 and DeepSeek V3 target overlapping workloads but differ sharply on economics. DeepSeek V3 runs roughly 16% cheaper on a blended input-plus-output token mix, which translates to approximately $1,134 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.
On capability surface area, the models diverge: DeepSeek R1 supports native reasoning mode 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 8 public benchmarks, DeepSeek R1 leads on 8 and DeepSeek V3 leads on 0. The widest gap is on arena-elo, where DeepSeek R1 scores 51.0 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
provider: azure-ai-foundry
fallback:
model: deepseek-r1
provider: azure-ai-foundry
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek R1 | DeepSeek V3 | |
|---|---|---|
| Input price | $1.35/M | $1.14/M |
| Output price | $5.40/M | $4.56/M |
| Context window | 128,000 | 128,000 |
| Max output | 8,192 | 8,192 |
| 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 | DeepSeek R1 | DeepSeek V3 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $729 /mo | $616 /mo | $113/mo |
| Mid-market 100K requests/day | $7,290 /mo | $6,156 /mo | $1,134/mo |
| Enterprise 1M requests/day | $72,900 /mo | $61,560 /mo | $11,340/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 runs ~16% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your tasks involve multi-step planning or math-heavy reasoning — DeepSeek R1 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
On arena-elo, DeepSeek R1 scores 51.0 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 DeepSeek R1, switching to DeepSeek V3 means re-architecting that path (and vice versa).
- • Native reasoning mode
Capabilities both share (1)
- ✓ 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 | DeepSeek R1 | DeepSeek V3 | Winner | Δ |
|---|---|---|---|---|
| aime-2024 | 79.8 | 39.6 | DeepSeek R1 | +40.2 |
| arena-elo | 1361.0 | 1310.0 | DeepSeek R1 | +51.0 |
| gpqa-diamond | 71.5 | 59.1 | DeepSeek R1 | +12.4 |
| humaneval | 89.7 | 82.6 | DeepSeek R1 | +7.1 |
| livecodebench | 65.9 | 40.5 | DeepSeek R1 | +25.4 |
| mmlu | 90.8 | 88.5 | DeepSeek R1 | +2.3 |
| mmlu-pro | 84.0 | 75.9 | DeepSeek R1 | +8.1 |
| swe-bench-verified | 49.2 | 42.0 | DeepSeek R1 | +7.2 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- DeepSeek R1 has capabilities DeepSeek V3 lacks: Native reasoning mode. Switching to DeepSeek V3 means re-architecting any flow that depends on these.
How to A/B test DeepSeek R1 vs DeepSeek V3 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 R1 primary, mirror 20% of traffic to DeepSeek V3 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 R1 vs DeepSeek V3
Which is cheaper, DeepSeek R1 or DeepSeek V3? ▾
DeepSeek V3 is cheaper by roughly 16% on a blended input + output token mix. Input prices are $1.35/M for DeepSeek R1 versus $1.14/M for DeepSeek V3; output prices are $5.40/M versus $4.56/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 R1 versus DeepSeek V3? ▾
DeepSeek R1 supports up to 128,000 tokens of context. DeepSeek V3 supports up to 128,000 tokens. DeepSeek V3 has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
When should I choose DeepSeek R1 over DeepSeek V3? ▾
Your tasks involve multi-step planning or math-heavy reasoning — DeepSeek R1 ships a native reasoning mode that explicitly thinks before responding, the other doesn't. On arena-elo, DeepSeek R1 scores 51.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose DeepSeek V3 over DeepSeek R1? ▾
You're cost-sensitive at scale — DeepSeek V3 runs ~16% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
How do I A/B test DeepSeek R1 against DeepSeek V3 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.