DeepSeek V4 Pro vs Qwen3.7 Max
DeepSeek V4 Pro (Novita AI, 1,048,576-token context) versus Qwen3.7 Max (Novita AI, 1,000,000-token context). DeepSeek V4 Pro is cheaper by 4% on a blended token mix. 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 V4 Pro vs Qwen3.7 Max
DeepSeek V4 Pro and Qwen3.7 Max are priced within 4% 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.
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: qwen-qwen3-7-max
provider: novita-ai
fallback:
model: deepseek-deepseek-v4-pro
provider: novita-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek V4 Pro | Qwen3.7 Max | |
|---|---|---|
| Input price | $1.60/M | $1.25/M |
| Output price | $3.20/M | $3.75/M |
| Context window | 1,048,576 | 1,000,000 |
| Max output | 393,216 | 65,536 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 2026 |
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 V4 Pro | Qwen3.7 Max | Delta |
|---|---|---|---|
| Startup 10K requests/day | $672 /mo | $600 /mo | $72.00/mo |
| Mid-market 100K requests/day | $6,720 /mo | $6,000 /mo | $720/mo |
| Enterprise 1M requests/day | $67,200 /mo | $60,000 /mo | $7,200/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.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 393,216 on DeepSeek V4 Pro vs 65,536 on Qwen3.7 Max. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
How to A/B test DeepSeek V4 Pro vs Qwen3.7 Max 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 V4 Pro primary, mirror 20% of traffic to Qwen3.7 Max 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 V4 Pro vs Qwen3.7 Max
Which is cheaper, DeepSeek V4 Pro or Qwen3.7 Max? ▾
DeepSeek V4 Pro is cheaper by roughly 4% on a blended input + output token mix. Input prices are $1.60/M for DeepSeek V4 Pro versus $1.25/M for Qwen3.7 Max; output prices are $3.20/M versus $3.75/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 V4 Pro versus Qwen3.7 Max? ▾
DeepSeek V4 Pro supports up to 1,048,576 tokens of context. Qwen3.7 Max supports up to 1,000,000 tokens. DeepSeek V4 Pro 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.
Do DeepSeek V4 Pro and Qwen3.7 Max both support tool calling? ▾
Yes — both DeepSeek V4 Pro and Qwen3.7 Max 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.
Which model supports prompt caching for cost reduction? ▾
Both DeepSeek V4 Pro and Qwen3.7 Max 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 V4 Pro against Qwen3.7 Max 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.