DeepSeek V4 Pro vs GPT Oss 120B
DeepSeek V4 Pro (DeepSeek, 1,000,000-token context) versus GPT Oss 120B (Cerebras, 131,072-token context). GPT Oss 120B is cheaper by 16% on a blended token mix. DeepSeek V4 Pro uniquely supports prompt caching. 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 GPT Oss 120B
DeepSeek V4 Pro and GPT Oss 120B target overlapping workloads but differ sharply on economics. GPT Oss 120B runs roughly 16% cheaper on a blended input-plus-output token mix, which translates to approximately $327 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.
DeepSeek V4 Pro ships a 1,000,000-token context window, 7.6x larger than GPT Oss 120B's 131,072 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 131,072 tokens, the extra context on DeepSeek V4 Pro is insurance you may never use — and GPT Oss 120B may win on other axes.
On capability surface area, the models diverge: DeepSeek V4 Pro supports prompt caching 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: gpt-oss-120b
provider: cerebras
fallback:
model: deepseek-v4-pro
provider: deepseek
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek V4 Pro | GPT Oss 120B | |
|---|---|---|
| Input price | $0.435/M | $0.350/M |
| Output price | $0.870/M | $0.750/M |
| Context window | 1,000,000 | 131,072 |
| Max output | 8,192 | 32,768 |
| 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 V4 Pro | GPT Oss 120B | Delta |
|---|---|---|---|
| Startup 10K requests/day | $183 /mo | $150 /mo | $32.70/mo |
| Mid-market 100K requests/day | $1,827 /mo | $1,500 /mo | $327/mo |
| Enterprise 1M requests/day | $18,270 /mo | $15,000 /mo | $3,270/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 — GPT Oss 120B runs ~16% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — DeepSeek V4 Pro fits 1,000,000 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot.
You re-send the same large system prompt across requests — DeepSeek V4 Pro supports prompt caching, cutting input cost on repeat hits.
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 V4 Pro, switching to GPT Oss 120B means re-architecting that path (and vice versa).
- • Prompt caching
Capabilities both share (5)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 87% when moving from DeepSeek V4 Pro (1,000,000) to GPT Oss 120B (131,072). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on DeepSeek V4 Pro vs 32,768 on GPT Oss 120B. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- DeepSeek V4 Pro has capabilities GPT Oss 120B lacks: Prompt caching. Switching to GPT Oss 120B means re-architecting any flow that depends on these.
- Provider changes from DeepSeek to Cerebras. 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 V4 Pro 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 V4 Pro 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 V4 Pro vs GPT Oss 120B
Which is cheaper, DeepSeek V4 Pro or GPT Oss 120B? ▾
GPT Oss 120B is cheaper by roughly 16% on a blended input + output token mix. Input prices are $0.435/M for DeepSeek V4 Pro versus $0.350/M for GPT Oss 120B; output prices are $0.870/M versus $0.750/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 GPT Oss 120B? ▾
DeepSeek V4 Pro supports up to 1,000,000 tokens of context. GPT Oss 120B supports up to 131,072 tokens. DeepSeek V4 Pro has the larger window by a factor of 7.6x, 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 GPT Oss 120B both support tool calling? ▾
Yes — both DeepSeek V4 Pro and 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.
Which model supports prompt caching for cost reduction? ▾
DeepSeek V4 Pro supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, DeepSeek V4 Pro gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose DeepSeek V4 Pro over GPT Oss 120B? ▾
Your workload needs long context — DeepSeek V4 Pro fits 1,000,000 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot. You re-send the same large system prompt across requests — DeepSeek V4 Pro supports prompt caching, cutting input cost on repeat hits.
When should I choose GPT Oss 120B over DeepSeek V4 Pro? ▾
You're cost-sensitive at scale — GPT Oss 120B 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 V4 Pro 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.