DeepSeek R1 vs GPT Oss 120B
DeepSeek R1 (Azure AI Foundry, 128,000-token context) versus GPT Oss 120B (SambaNova, 131,072-token context). DeepSeek R1 is cheaper by 10% on a blended token mix. GPT Oss 120B 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 R1 vs GPT Oss 120B
DeepSeek R1 and GPT Oss 120B target overlapping workloads but differ sharply on economics. DeepSeek R1 runs roughly 10% cheaper on a blended input-plus-output token mix, which translates to approximately $4,410 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: GPT Oss 120B 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-r1
provider: azure-ai-foundry
fallback:
model: gpt-oss-120b
provider: sambanova
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek R1 | GPT Oss 120B | |
|---|---|---|
| Input price | $1.35/M | $3.00/M |
| Output price | $5.40/M | $4.50/M |
| Context window | 128,000 | 131,072 |
| Max output | 8,192 | 131,072 |
| 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 | GPT Oss 120B | Delta |
|---|---|---|---|
| Startup 10K requests/day | $729 /mo | $1,170 /mo | $441/mo |
| Mid-market 100K requests/day | $7,290 /mo | $11,700 /mo | $4,410/mo |
| Enterprise 1M requests/day | $72,900 /mo | $117,000 /mo | $44,100/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 — GPT Oss 120B 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 R1, switching to GPT Oss 120B means re-architecting that path (and vice versa).
- • Function calling
Capabilities both share (2)
- ✓ Streaming
- ✓ 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: 8,192 on DeepSeek R1 vs 131,072 on GPT Oss 120B. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT Oss 120B has capabilities DeepSeek R1 lacks: Function calling. Worth wiring through the agent design before commit.
- Provider changes from Azure AI Foundry to SambaNova. 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 R1 vs 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 R1 primary, mirror 20% of traffic to 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 R1 vs GPT Oss 120B
Which is cheaper, DeepSeek R1 or GPT Oss 120B? ▾
DeepSeek R1 is cheaper by roughly 10% on a blended input + output token mix. Input prices are $1.35/M for DeepSeek R1 versus $3.00/M for GPT Oss 120B; output prices are $5.40/M versus $4.50/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 GPT Oss 120B? ▾
DeepSeek R1 supports up to 128,000 tokens of context. GPT Oss 120B supports up to 131,072 tokens. 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 R1 and GPT Oss 120B both support tool calling? ▾
Only GPT Oss 120B 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.
How do I A/B test DeepSeek R1 against 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.