AI model comparison
Mercury 2.5 vs gpt-oss-120b
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Inception
Mercury 2.5
Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception.
- Input / 1M
- $0.04
- Output / 1M
- $0.15
- Context
- 260K
OpenAI
gpt-oss-120b
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases.
- Input / 1M
- $0.04
- Output / 1M
- $0.17
- Context
- 131K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Mercury 2.5
1.0x cheaper for a typical chat
Higher intelligence score
Mercury 2.5
12.3 vs 11.6 on Artificial Analysis
Larger context window
Mercury 2.5
260K vs 131K tokens
Newer release
Mercury 2.5
Released September 8, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Mercury 2.512.3gpt-oss-120b11.6Coding index
Mercury 2.5Not availablegpt-oss-120b30.4Agentic index
Mercury 2.5Not availablegpt-oss-120b3.7Pricing
Mercury 2.5 vs gpt-oss-120b API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Mercury 2.5 | gpt-oss-120b |
|---|---|---|
| Input tokensPer 1M tokens | $0.04 | $0.04 |
| Output tokensPer 1M tokens | $0.15 | $0.17 |
| Cached input (read)Per 1M tokens | $0.00 | Not available |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Mercury 2.5$0.16gpt-oss-120b$0.16Coding task
30K in, 4K out
Mercury 2.5$1.80$0.72 cachedgpt-oss-120b$1.79Long document summary
150K in, 2K out
Mercury 2.5$6.30$0.90 cachedgpt-oss-120b$5.89Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Mercury 2.5 | gpt-oss-120b |
|---|---|---|
| Context window | 260K tokens | 131K tokens |
| Max output | 66K tokens | 118K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | High, Medium, Low, None | High, Medium, Low |
| Default reasoning effort | Medium | Medium |
| Release date | September 8, 2026 | August 5, 2025 |
Features
Supported features
API features available for each model.
| Metric | Mercury 2.5 | gpt-oss-120b |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Not supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
gpt-oss-120bView full model details- gpt-oss-120b vs Muse Spark 1.3 Contributor
- gpt-oss-120b vs Gemini 2.5 Flash Lite
- gpt-oss-120b vs Gemma 4 31B
- gpt-oss-120b vs Qwen3.7 Flash
- gpt-oss-120b vs gpt-oss-20b
- gpt-oss-120b vs GPT-5 Nano
- gpt-oss-120b vs Qwen3 235B A22B Instruct 2507
- gpt-oss-120b vs Qwen3.5-9B
- gpt-oss-120b vs Nemotron 3.5 Lightning
- gpt-oss-120b vs GPT-4.1 Nano
- gpt-oss-120b vs Granite 4.2 8B
- gpt-oss-120b vs Nemotron 3 Super
- gpt-oss-120b vs Mistral Small 4
- gpt-oss-120b vs Qwen3 Coder 30B A3B Instruct
- gpt-oss-120b vs Nemotron 3 Nano 30B A3B
Our take
Mercury 2.5 is 1.0x cheaper for a typical chat. Mercury 2.5 scores higher on the Artificial Analysis Intelligence Index (12.3 vs 11.6). Mercury 2.5 has the larger context window (260K vs 131K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Mercury 2.5 or gpt-oss-120b cheaper?
Mercury 2.5 is cheaper for a typical chat (2K input tokens and 500 output tokens). Mercury 2.5 costs $0.04 per 1M input tokens and $0.15 per 1M output tokens, while gpt-oss-120b costs $0.04 per 1M input tokens and $0.17 per 1M output tokens.
Which has a bigger context window, Mercury 2.5 or gpt-oss-120b?
Mercury 2.5 supports up to 260K tokens of context, compared with 131K for gpt-oss-120b.
Is Mercury 2.5 smarter than gpt-oss-120b?
On the Artificial Analysis Intelligence Index, Mercury 2.5 scores 12.3 and gpt-oss-120b scores 11.6, putting Mercury 2.5 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Mercury 2.5 and gpt-oss-120b at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Mercury 2.5 and gpt-oss-120b answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Mercury 2.5 and gpt-oss-120b at the same time. Compare the answers side by side and keep the best one.