OpenAI GPT Oss 120B vs Qwen Qwen3.235b A22b Thinking 2507
OpenAI GPT Oss 120B (Groq, 131,072-token context) versus Qwen Qwen3.235b A22b Thinking 2507 (OpenRouter, 262,144-token context). Qwen Qwen3.235b A22b Thinking 2507 is cheaper by 5% on a blended token mix. OpenAI GPT Oss 120B uniquely supports parallel tool calls and structured output (json schema). 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 Qwen Qwen3.235b A22b Thinking 2507
OpenAI GPT Oss 120B and Qwen Qwen3.235b A22b Thinking 2507 are priced within 5% 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.
Qwen Qwen3.235b A22b Thinking 2507 ships a 262,144-token context window, 2.0x larger than OpenAI 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 Qwen Qwen3.235b A22b Thinking 2507 is insurance you may never use — and OpenAI GPT Oss 120B may win on other axes.
On capability surface area, the models diverge: OpenAI GPT Oss 120B supports parallel tool calls where the other does not; OpenAI GPT Oss 120B supports structured output (json schema) 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: qwen-qwen3-235b-a22b-thinking-2507
provider: openrouter
fallback:
model: openai-gpt-oss-120b
provider: groq
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| OpenAI GPT Oss 120B | Qwen Qwen3.235b A22b Thinking 2507 | |
|---|---|---|
| Input price | $0.150/M | $0.110/M |
| Output price | $0.600/M | $0.600/M |
| Context window | 131,072 | 262,144 |
| Max output | 32,766 | 262,144 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | — | — |
| Structured output | ✓ | — |
| Pricing verified | Jun 2, 2026 | Jun 2, 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 | OpenAI GPT Oss 120B | Qwen Qwen3.235b A22b Thinking 2507 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $81.00 /mo | $69.00 /mo | $12.00/mo |
| Mid-market 100K requests/day | $810 /mo | $690 /mo | $120/mo |
| Enterprise 1M requests/day | $8,100 /mo | $6,900 /mo | $1,200/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.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — Qwen Qwen3.235b A22b Thinking 2507 fits 262,144 tokens versus the other model's 131,072, enough headroom for full books, large codebases, or 100+ page documents in one shot.
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 Qwen Qwen3.235b A22b Thinking 2507 means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Structured output (JSON schema)
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 100% when moving from OpenAI GPT Oss 120B (131,072) to Qwen Qwen3.235b A22b Thinking 2507 (262,144). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 32,766 on OpenAI GPT Oss 120B vs 262,144 on Qwen Qwen3.235b A22b Thinking 2507. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- OpenAI GPT Oss 120B has capabilities Qwen Qwen3.235b A22b Thinking 2507 lacks: Parallel tool calls, Structured output (JSON schema). Switching to Qwen Qwen3.235b A22b Thinking 2507 means re-architecting any flow that depends on these.
- Provider changes from Groq 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 OpenAI GPT Oss 120B vs Qwen Qwen3.235b A22b Thinking 2507 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 OpenAI GPT Oss 120B primary, mirror 20% of traffic to Qwen Qwen3.235b A22b Thinking 2507 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 — OpenAI GPT Oss 120B vs Qwen Qwen3.235b A22b Thinking 2507
Which is cheaper, OpenAI GPT Oss 120B or Qwen Qwen3.235b A22b Thinking 2507? ▾
Qwen Qwen3.235b A22b Thinking 2507 is cheaper by roughly 5% on a blended input + output token mix. Input prices are $0.150/M for OpenAI GPT Oss 120B versus $0.110/M for Qwen Qwen3.235b A22b Thinking 2507; output prices are $0.600/M versus $0.600/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 Qwen Qwen3.235b A22b Thinking 2507? ▾
OpenAI GPT Oss 120B supports up to 131,072 tokens of context. Qwen Qwen3.235b A22b Thinking 2507 supports up to 262,144 tokens. Qwen Qwen3.235b A22b Thinking 2507 has the larger window by a factor of 2.0x, 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 Qwen Qwen3.235b A22b Thinking 2507 both support tool calling? ▾
Yes — both OpenAI GPT Oss 120B and Qwen Qwen3.235b A22b Thinking 2507 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 OpenAI GPT Oss 120B against Qwen Qwen3.235b A22b Thinking 2507 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.