DeepSeek DeepSeek v3.1 vs OpenAI GPT Oss 120B
DeepSeek DeepSeek v3.1 (Novita AI, 131,072-token context) versus OpenAI GPT Oss 120B (OpenRouter, 131,072-token context). OpenAI GPT Oss 120B is cheaper by 23% 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 DeepSeek v3.1 vs OpenAI GPT Oss 120B
DeepSeek DeepSeek v3.1 and OpenAI GPT Oss 120B target overlapping workloads but differ sharply on economics. OpenAI GPT Oss 120B runs roughly 23% cheaper on a blended input-plus-output token mix, which translates to approximately $390 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.
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: openai-gpt-oss-120b
provider: openrouter
fallback:
model: deepseek-deepseek-v3-1
provider: novita-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek DeepSeek v3.1 | OpenAI GPT Oss 120B | |
|---|---|---|
| Input price | $0.270/M | $0.180/M |
| Output price | $1.00/M | $0.800/M |
| Context window | 131,072 | 131,072 |
| Max output | 32,768 | 32,768 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | — | — |
| Structured output | ✓ | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 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 DeepSeek v3.1 | OpenAI GPT Oss 120B | Delta |
|---|---|---|---|
| Startup 10K requests/day | $141 /mo | $102 /mo | $39.00/mo |
| Mid-market 100K requests/day | $1,410 /mo | $1,020 /mo | $390/mo |
| Enterprise 1M requests/day | $14,100 /mo | $10,200 /mo | $3,900/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 — OpenAI GPT Oss 120B runs ~23% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Provider changes from Novita AI to OpenRouter. 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 DeepSeek DeepSeek v3.1 vs OpenAI GPT Oss 120B 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 DeepSeek v3.1 primary, mirror 20% of traffic to OpenAI GPT Oss 120B 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 DeepSeek v3.1 vs OpenAI GPT Oss 120B
Which is cheaper, DeepSeek DeepSeek v3.1 or OpenAI GPT Oss 120B? ▾
OpenAI GPT Oss 120B is cheaper by roughly 23% on a blended input + output token mix. Input prices are $0.270/M for DeepSeek DeepSeek v3.1 versus $0.180/M for OpenAI GPT Oss 120B; output prices are $1.00/M versus $0.800/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 DeepSeek v3.1 versus OpenAI GPT Oss 120B? ▾
DeepSeek DeepSeek v3.1 supports up to 131,072 tokens of context. OpenAI GPT Oss 120B supports up to 131,072 tokens. OpenAI GPT Oss 120B 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 DeepSeek v3.1 and OpenAI GPT Oss 120B both support tool calling? ▾
Yes — both DeepSeek DeepSeek v3.1 and OpenAI GPT Oss 120B 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.
How do I A/B test DeepSeek DeepSeek v3.1 against OpenAI GPT Oss 120B 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.