OpenAI GPT Oss 120B vs OpenAI GPT Oss 20B

OpenAI GPT Oss 120B (Novita AI, 131,072-token context) versus OpenAI GPT Oss 20B (Groq, 131,072-token context). OpenAI GPT Oss 120B is cheaper by 20% on a blended token mix. OpenAI GPT Oss 120B uniquely supports vision input. 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 — OpenAI GPT Oss 120B vs OpenAI GPT Oss 20B

OpenAI GPT Oss 120B and OpenAI GPT Oss 20B target overlapping workloads but differ sharply on economics. OpenAI GPT Oss 120B runs roughly 20% cheaper on a blended input-plus-output token mix, which translates to approximately $105 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: OpenAI GPT Oss 120B supports vision input 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.

Side-by-side cost

Live workload comparison

Same workload run through both models. The cheaper one is highlighted.

3,000
0131,072
400
032,768
5,000
01,000,000
Novita AI
$38.05/mo
Input $0.0500/M · Output $0.250/M
Groq
$52.50/mo
Input $0.0750/M · Output $0.300/M
At this workload, OpenAI GPT Oss 120B is 28% cheaper than OpenAI GPT Oss 20B — a savings of $14.46/month ($173/year).
Production recipe — Agent Command Center
strategy: cost-optimized
primary:
  model: openai-gpt-oss-120b
  provider: novita-ai
fallback:
  model: openai-gpt-oss-20b
  provider: groq
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live
OpenAI GPT Oss 120B OpenAI GPT Oss 20B
Input price $0.0500/M $0.0750/M
Output price $0.250/M $0.300/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 Jun 2, 2026 Jun 2, 2026
Cheaper option
~20% cheaper than the priciest in this pair
Larger context
131,072 tokens
More capabilities
4 of 6 capability flags advertised

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 OpenAI GPT Oss 120B OpenAI GPT Oss 20B Delta
Startup
10K requests/day
$30.00 /mo $40.50 /mo $10.50/mo
Mid-market
100K requests/day
$300 /mo $405 /mo $105/mo
Enterprise
1M requests/day
$3,000 /mo $4,050 /mo $1,050/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.

Choose OpenAI GPT Oss 120B

You're cost-sensitive at scale — OpenAI GPT Oss 120B runs ~20% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.

Choose OpenAI GPT Oss 120B

Your inputs include screenshots, diagrams, or product photos — OpenAI GPT Oss 120B accepts image input natively, the other doesn't.

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 OpenAI GPT Oss 120B, switching to OpenAI GPT Oss 20B means re-architecting that path (and vice versa).

Only on OpenAI GPT Oss 120B
  • • Vision input
Only on OpenAI GPT Oss 20B
Nothing — everything OpenAI GPT Oss 20B ships is also on OpenAI GPT Oss 120B.
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.

  • OpenAI GPT Oss 120B has capabilities OpenAI GPT Oss 20B lacks: Vision input. Switching to OpenAI GPT Oss 20B means re-architecting any flow that depends on these.
  • Provider changes from Novita AI to Groq. 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 OpenAI GPT Oss 120B vs OpenAI GPT Oss 20B 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. 1. Point your existing OpenAI SDK at https://gateway.futureagi.com/v1. No code change beyond base_url and a virtual key.
  2. 2. Mark OpenAI GPT Oss 120B primary, mirror 20% of traffic to OpenAI GPT Oss 20B in shadow mode. Both responses are logged; only the primary is served to users.
  3. 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. 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 — OpenAI GPT Oss 120B vs OpenAI GPT Oss 20B

Which is cheaper, OpenAI GPT Oss 120B or OpenAI GPT Oss 20B?

OpenAI GPT Oss 120B is cheaper by roughly 20% on a blended input + output token mix. Input prices are $0.0500/M for OpenAI GPT Oss 120B versus $0.0750/M for OpenAI GPT Oss 20B; output prices are $0.250/M versus $0.300/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 OpenAI GPT Oss 120B versus OpenAI GPT Oss 20B?

OpenAI GPT Oss 120B supports up to 131,072 tokens of context. OpenAI GPT Oss 20B supports up to 131,072 tokens. OpenAI GPT Oss 20B 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 OpenAI GPT Oss 120B and OpenAI GPT Oss 20B both support tool calling?

Yes — both OpenAI GPT Oss 120B and OpenAI GPT Oss 20B 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.

Can OpenAI GPT Oss 120B and OpenAI GPT Oss 20B process images?

OpenAI GPT Oss 120B accepts native image input. OpenAI GPT Oss 20B does not — you would need to route image-heavy workloads through OpenAI GPT Oss 120B or add a separate vision model in front of OpenAI GPT Oss 20B.

When should I choose OpenAI GPT Oss 120B over OpenAI GPT Oss 20B?

You're cost-sensitive at scale — OpenAI GPT Oss 120B runs ~20% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your inputs include screenshots, diagrams, or product photos — OpenAI GPT Oss 120B accepts image input natively, the other doesn't.

When should I choose OpenAI GPT Oss 20B over OpenAI GPT Oss 120B?

On the data this page surfaces, OpenAI GPT Oss 20B is the right pick when OpenAI GPT Oss 120B's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.

How do I A/B test OpenAI GPT Oss 120B against OpenAI GPT Oss 20B 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.