GPT-5.6 Luna vs MiniMax M3
GPT-5.6 Luna is OpenAI's affordable GPT-5.6 tier. MiniMax M3 is an open-weight model built around sparse attention, native image/video understanding and long-running agents. On published coding numbers Luna leads; on BrowseComp M3 is fractionally ahead. The real choice is between managed frontier tooling and a remarkably cheap multimodal agent.
Specifications and capability envelope
| Specification | GPT-5.6 Luna | MiniMax M3 |
|---|---|---|
| Provider / release | OpenAI · July 2026 GA | MiniMax · June 1, 2026 |
| API model ID | gpt-5.6-luna | MiniMax-M3 |
| Architecture | Proprietary | MiniMax Sparse Attention (MSA) |
| Context window | 1.05M tokens | Up to 1M; official model page says 512K minimum is guaranteed |
| Maximum output | 128K tokens | 512K maximum; 128K recommended by API docs |
| Input / output | Text and images / text | Text, images and video / text |
| Reasoning | GPT-5.6 reasoning tier | Thinking can be enabled or disabled |
| Tools / agents | Hosted web, file, code, shell, computer use, MCP, tool search | Tool calls, agentic workflows, computer use and MiniMax Code |
| Weights / license | Closed, proprietary API | Open-weight/open-source release; verify current repository terms |
| Image and video input | Image input | Native image and video input |
MSA is MiniMax's central technical bet. The company says its sparse attention design reduces per-token compute at 1M context to one-twentieth of the prior generation, with more than 9× faster prefill and more than 15× faster decoding in its comparisons. These are architectural and vendor-reported claims, not a guarantee of the same throughput on every provider or hardware stack.
Published benchmark comparison
Luna's figures below come from OpenAI's GPT-5.6 scorecard. M3's figures come from MiniMax's release materials and model page. The evaluations use different scaffolds and infrastructure, so compare the task definition and harness alongside the number.
| Evaluation | Luna | MiniMax M3 | Reading |
|---|---|---|---|
| SWE-Bench Pro | 62.7% | 59.0% | Luna +3.7 · coding edge |
| Terminal-Bench 2.1 | 84.7% | 66.0% | Luna +18.7 · infrastructure differs |
| BrowseComp | 83.3% | 83.5% | M3 +0.2 · near tie |
| MCP Atlas public | Not published by OpenAI | 74.2% | M3-only public result |
| PostTrainBench | Not in Luna's official table | 37.1 | M3-only result; model page reports rank #3 |
| GPQA Diamond | 92.3% | Not in MiniMax's release table | Do not fill the gap with a different model variant |
| OSWorld | 45.6% on OSWorld 2.0 | 70.06% on M3's 200-step setup | Different protocol; directional only |
Radar: benchmark evidence plus product capabilities
● Luna ● M3
The radar uses three shared public benchmark values and a context-capacity axis. Context is a documented maximum, not a quality score. The chart should be read as a decision aid.
- Luna's polygon extends further on the cited coding and terminal rows.
- M3 reaches the same browsing tier in the cited provider scores.
- Both models have roughly million-token context; M3's modality advantage is not represented in this benchmark radar.
Coding and agents: different strengths
Where Luna is stronger
Luna's 62.7% SWE-Bench Pro and 84.7% Terminal-Bench 2.1 results make it the more obvious first choice for repository repair, CLI automation and coding agents if you want to stay inside a managed OpenAI stack. The GPT-5.6 family also exposes programmatic tool calling, hosted shell, computer use and tool search, which can reduce application-side orchestration work.
For teams that care about a broad, independently documented evaluation portfolio, OpenAI publishes Luna rows for coding, science, professional work, computer use, multimodal reasoning and long context.
Where M3 is stronger
M3's official release focuses on long-running collaboration rather than only one-shot code repair. MiniMax reports a 12-hour paper-reproduction run with 18 commits and 23 figures, and a CUDA optimization run with 147 submissions and 1,959 tool calls. These are demonstrations, not standardized leaderboard scores, but they show the intended operating mode.
Native video input, computer use and an open-weight path make M3 unusually interesting for multimodal agents, media-rich workflows and teams willing to build their own harness.
| Use case | Advantage | Why |
|---|---|---|
| Repository bug fixing | Luna | Higher published SWE-Bench Pro score. |
| Terminal automation | Luna on published result | M3's score uses a different internal setup; validate before ruling it out. |
| Web research and browsing | Near tie / M3 +0.2 | MiniMax model page reports 83.5 on BrowseComp versus Luna's 83.3. |
| Video understanding | M3 | Native video input; Luna's cited API supports image input, not video. |
| Hosted tool ecosystem | Luna | OpenAI exposes a broad managed Responses API surface. |
| Open deployment and fine-tuning | M3 | Open-weight distribution; confirm current license and hardware requirements. |
Pricing: M3 is dramatically cheaper, even at long context
| Standard API rate per 1M | GPT-5.6 Luna | MiniMax M3 |
|---|---|---|
| Fresh input, normal context | $1.00 | $0.30 for ≤512K input |
| Cached input | $0.10 | $0.06 for ≤512K input |
| Output | $6.00 | $1.20 for ≤512K input |
| Long-context input > threshold | $2.00 when request exceeds 272K | $0.60 when input exceeds 512K |
| Long-context output | $9.00 when request exceeds 272K | $2.40 when input exceeds 512K |
| Long-context cache read | $0.20 | $0.12 |
M3's standard example is about 3.8× cheaper than Luna. For a genuinely long request, the same 10M input plus 1M output would be approximately $8.40 on M3's long-context rate and $29 on Luna's long-context rate. The exact bill depends on how many requests cross each provider's threshold, cache hits, output length, service tier and tool charges.
Context and multimodality
Luna
- 1.05M-token context and 128K maximum output.
- Text and image input, text output.
- OpenAI official results include image understanding, computer use, web browsing and long-context retrieval.
- Useful if the rest of your application already uses Responses API tools.
M3
- Up to 1M context with a 512K minimum guarantee on the official model page.
- Text, image and video input with text output.
- 512K maximum output; MiniMax recommends 128K for ordinary calls.
- MSA is designed for long-range agents, long coding sessions and long-video understanding.
Both models advertise a million-token class window, but a maximum context is not a promise that every token remains equally useful. Use retrieval, compaction and state summaries. If your application is video-heavy, M3 has a fundamental input-modality advantage; if it is tool-heavy and image-based, Luna's hosted tool layer may reduce integration effort.
Who should choose which?
Choose GPT-5.6 Luna if…
- You want the stronger published coding and terminal profile without operating model infrastructure.
- You need OpenAI's hosted web search, file search, code execution, computer use, MCP or tool search.
- You mostly process text and images, and 128K output is enough.
- You need a clear evaluation portfolio for procurement and model-risk review.
Choose MiniMax M3 if…
- Video input, native multimodality and long-running agent collaboration are central.
- You need far lower token cost at high volume or want to experiment with open weights.
- You need a million-token window and may benefit from a 512K maximum output.
- You can validate MiniMax's scaffolding, latency, concurrency and current license in your own deployment.
Test Luna and M3 on your own workload
Use identical prompts, tool definitions, acceptance tests and token budgets. Benchmark the complete agent, not just the base model.
Try both in CodingFleet →Bottom line
GPT-5.6 Luna is the better documented coding default. It leads M3 on the cited SWE-Bench Pro and Terminal-Bench rows and provides a mature managed tool ecosystem.
MiniMax M3 is the more ambitious value proposition. Its MSA architecture targets million-token agents, it accepts video as well as images, its BrowseComp result is fractionally ahead of Luna's, and its standard API price is dramatically lower.
If the job is conventional repository coding and you value managed infrastructure, start with Luna. If the job is multimodal, long-running, open-weight or cost-sensitive, M3 deserves a serious evaluation rather than being dismissed as a cheaper alternative.
Sources and methodology
- OpenAI — GPT-5.6 and GPT-5.6 Luna API docs. Luna benchmarks, tools, modalities and pricing.
- MiniMax — M3 release article and MiniMax M3 model page. Architecture, modalities, demonstrations and benchmark claims.
- MiniMax API model invocation docs and Messages API reference. Context and output limits.
- MiniMax benchmark numbers are vendor-reported and often use MiniMax's internal infrastructure. The radar and bars are visual aids; benchmark versions, scaffolds, step limits and provider conditions should be reproduced before a production decision.