gpt-oss-120b vs Mercury 2.5 Preview
OpenAI · US | Inception Labs · US · Updated June 2026
Quick verdict
Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). Choose gpt-oss-120b if you need self-hosting or data privacy; Mercury 2.5 Preview if you want a managed API.
gpt-oss-120b (OpenAI) and Mercury 2.5 Preview (Inception Labs) are two of the models people most often weigh against each other in 2026. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Cost model: gpt-oss-120b ships open weights you can self-host (hardware cost only, no per-token fee), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Mercury 2.5 Preview holds 2× more — 260K tokens (~390 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Mercury 2.5 Preview is the newer model by about 13 months (released August 31, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | gpt-oss-120b | Mercury 2.5 Preview |
|---|---|---|
| Provider | OpenAI (US) | Inception Labs (US) |
| Released | August 5, 2025 | August 31, 2026 |
| Context window | 131K (~197 pages) | 260K tokens (~390 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.04/$0.15 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, code | text |
| SWE-Bench Verified | 62.4% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
Open weights make this possible at all — Mercury 2.5 Preview is API-only, so it cannot leave the vendor's servers.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use — and its weights are open while Mercury 2.5 Preview is API-only.
Agentic tool use, function calling, and code execution
gpt-oss-120b
gpt-oss-120b lists agentic tool use, function calling, and code execution among its strengths; Mercury 2.5 Preview does not.
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
Mercury 2.5 Preview
Its 260K tokens window holds about 2× more than gpt-oss-120b's 131K in a single prompt.
Coding accuracy (95.7%, 91st percentile among cost-optimized models)
Mercury 2.5 Preview
gpt-oss-120b is comparatively weak here — 131K context and 5.1B active params trail the largest frontier closed models
Mathematics accuracy (97.0%, 97th percentile)
Mercury 2.5 Preview
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it carries the larger 260K tokens context.
Lowest cost at scale
gpt-oss-120b
Its weights are open, so at volume you pay for your own hardware instead of Mercury 2.5 Preview's $0.04/$0.15 per 1M tokens.
Largest single-prompt input
Mercury 2.5 Preview
Its 260K tokens window is about 2× larger than gpt-oss-120b's 131K, fitting roughly 390 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ gpt-oss-120b
At Open weight (self-host / free) it undercuts Mercury 2.5 Preview, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Mercury 2.5 Preview
Larger 260K tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ gpt-oss-120b
Open weights let you run it on your own hardware; Mercury 2.5 Preview is API-only.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
It is specifically built for that.
Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
→ Mercury 2.5 Preview
That is its strongest area.
gpt-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Mercury 2.5 Preview: where it fits
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.
Its trade-offs: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. gpt-oss-120b gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Mercury 2.5 Preview gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both gpt-oss-120b and Mercury 2.5 Preview without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is gpt-oss-120b or Mercury 2.5 Preview better for coding?
Public SWE-Bench figures are not available for Mercury 2.5 Preview, so the honest test is your own repository — run an identical real bug through both. By design, gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4 while Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, gpt-oss-120b or Mercury 2.5 Preview?
gpt-oss-120b is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Mercury 2.5 Preview — 260K tokens vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and Mercury 2.5 Preview together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, Mercury 2.5 Preview and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, gpt-oss-120b or Mercury 2.5 Preview?
Mercury 2.5 Preview — released August 31, 2026, about 13 months after gpt-oss-120b.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.