MiniMax M3 Release Date: Out June 2026, No M3.1 Yet

Verified against MiniMax's own model card, API docs and Hugging Face org listing on 31 August 2026. Every date, price and score below comes from a primary source checked on that date.

If you searched for a MiniMax M3 release date, you almost certainly also want to know whether something newer has landed since. Both answers are below, along with a full release timeline for the M-series and the models MiniMax has actually shipped in the three months since M3.

When was MiniMax M3 released?

MiniMax announced M3 on 1 June 2026. The launch went live on the evening of Sunday 31 May US Eastern time, which is why some coverage dates it to 31 May — it is the same event in two timezones. VentureBeat's launch piece is dated 1 June 2026.

The open weights followed almost immediately. The MiniMaxAI/MiniMax-M3 repository was created on 2 June 2026 at 07:49 UTC, with the quantised MiniMax-M3-MXFP8 repo created one minute later. That is the single most reliable release-date artefact available, because Hugging Face timestamps repository creation and does not let it be backdated.

The technical paper behind the model — MiniMax Sparse Attention, arXiv:2606.13392 — was submitted later, on 11 June 2026. If you are trying to cite M3 academically, that is the reference; if you are trying to date the release, use 1–2 June.

Here is where M3 sits in the full MiniMax lineage. Dates are Hugging Face repository creation dates from the official MiniMaxAI org, checked today.

Is there a MiniMax M3 Pro or an M3.1?

No. Neither exists as of 31 August 2026. This is worth stating plainly because a lot of search traffic assumes otherwise.

Three independent checks, all run today:

• Hugging Face. The MiniMaxAI org contains exactly two M3-generation repos: MiniMax-M3 and MiniMax-M3-MXFP8. There is no MiniMax-M3-Pro and no MiniMax-M3.1.
• The API model list. MiniMax's models overview lists MiniMax-M3, M2.7, M2.7-highspeed, and the legacy M2.5 / M2.1 / M2 endpoints. No M3 Pro, no M3.1, no other M3 tier.
• The product site. MiniMax's own navigation lists M3, M2.7, M2.5, H3, Speech 2.8 and Music 3.0. Again, no M3 variant beyond M3 itself.

Why do people expect one? Because MiniMax has shipped point releases at a steady clip before — M2 → M2.1 → M2.5 → M2.7 landed in roughly six months. On that precedent an M3.1 is plausible; the M2 line got its first point release about two months after the base model, and M3 is now approaching three. But plausible is not shipped. If someone quotes you an M3.1 release date today, they are guessing.

The one thing that genuinely looks like an "M3 variant" is MiniMax-M3-MXFP8 — the officially quantised build published the same day as the base model. It is the same model at lower precision for self-hosting, not a stronger tier. MiniMax's own deployment recipe uses it.

What has MiniMax shipped since M3?

Plenty — just not a new language model. The M3 repository itself was last updated on 23 July 2026 (documentation and config, not a version bump). The genuinely new releases since June have all been in other modalities:

• MiniMax H3 (28 July 2026) — an omni-modal video generation model. It takes text, image, video and audio context and generates video with synchronised stereo audio at up to 2K resolution, 24 FPS, in 4–15 second clips, with stable support for 11 languages. Architecturally it is nothing like M3: a dense 33B-parameter transformer (the H3-Omni-Transformer), not a sparse MoE. It ships under a separate MiniMax H3 Community License Agreement and has already pulled over 5.3 million downloads. An H3 Max high-speed variant is served through fal.ai. H3 is not "M3 for reasoning, but newer" — if you are choosing a text or coding model, H3 is irrelevant to you.
• MiniMax Music 3 (7 August 2026) — music generation, exposed as music-3.0. Note that MiniMax's docs state the paid music APIs are unavailable to new users after 20 August 2026.
• Speech 2.8 — the current text-to-speech line, with hd and turbo tiers covering 40 languages.

So the honest summary of MiniMax's summer: the language-model roadmap has been quiet since 1 June while the company pushed hard into video and audio generation.

What is MiniMax M3, technically?

Per the official model card, M3 is a 428B-parameter Mixture-of-Experts model that activates roughly 23B parameters per token, trained natively multimodal — mixed text, image and video from the first training step rather than a vision adapter bolted on afterwards. It serves a 1,000,000-token context window and can emit up to 262,144 completion tokens in a single response.

The engineering that makes the long context affordable is MiniMax Sparse Attention (MSA). The MSA paper describes it as blockwise sparse attention built on top of Grouped Query Attention, split into an Index Branch and a Main Branch. The reported effect at 1M context is a 28.4× reduction in attention computation, translating to 14.2× prefill and 7.6× decoding wall-clock speedups on H800 hardware in the paper's own measurements. MiniMax's model card quotes the end-to-end figure differently — 9× prefill and 15× decode speedup versus M2 at 1M context, with per-token compute cut to roughly 1/20.

Two behavioural details matter if you are actually building on it:

• Three reasoning modes: enabled, adaptive, and disabled. Adaptive lets the model decide when a query warrants a thinking pass, which is the practical default for mixed workloads.
• Interleaved thinking across tool calls. M3 reasons between each round of tool interaction rather than only at the start. MiniMax's tool-use documentation is explicit that you must append the complete assistant message — including the thinking content — back into conversation history, or the reasoning chain breaks and quality degrades. This is the most common integration mistake with M3.

Recommended sampling parameters from the model card are temperature=1.0 and top_p=0.95. Supported inference stacks are SGLang, vLLM, Transformers, KTransformers, unsloth and ATOM.

How does MiniMax M3 actually benchmark?

Separate these two tables. The first is what MiniMax reports; the second is what an independent evaluator measures. They tell noticeably different stories, and the gap is the most important thing on this page.

Vendor-reported, from the M3 model card and the 1 June launch coverage:

At launch that 59.0 on SWE-bench Pro was the headline, because it put M3 ahead of GPT-5.5 and Gemini 3.1 Pro on that specific benchmark at a fraction of their token price. That framing was accurate on 1 June.

Independently measured, from Artificial Analysis (Intelligence Index v4.1.1, an aggregate of nine evaluations across reasoning, coding and knowledge), checked today:

M3 scores 45 and ranks #11 of 111 on that index. It is not a leader on aggregate intelligence three months after launch — Kimi K3, GLM-5.3, Qwen3.8 and DeepSeek V4 Pro have all shipped since and all score higher. What M3 still wins on is cost and context: at a blended $0.20 per million tokens and $0.14 per Intelligence Index task it is the cheapest model in that top group, it serves 150 output tokens/sec with a 1.12s time-to-first-token, and nothing else on the list offers a 1M-token window.

The lesson is a general one for open-weights models: a launch-day benchmark table has a shelf life of roughly one quarter. Re-check before you standardise on anything.

How much does MiniMax M3 cost?

Current pay-as-you-go pricing from MiniMax's official rate card, verified today. Note that the price now tiers on prompt length — this is a change from launch, when the $0.30 / $1.20 rate was announced as a one-week promotion against a $0.60 / $2.40 list price. The cheap rate stuck; it is now the standard tier for prompts under 512K.

The 512K threshold is a real budgeting concern if you are using the 1M window as a selling point: crossing it doubles your rate on the entire request, not just the overflow. For long-document workloads, aggressive prompt caching at $0.06/M is where the savings actually live.

If you would rather pay a flat rate, MiniMax also sells coding subscriptions with 5-hour rolling and weekly quota windows: Plus at $22/month (3–4 concurrent agents), Max at $55/month (4–5 agents), and Ultra at $132/month (6–7 agents). All tiers include the full model lineup.

How do you access or self-host MiniMax M3?

Three routes, in ascending order of effort:

• Hosted API. MiniMax exposes both an OpenAI-compatible and an Anthropic-compatible endpoint, so most existing clients work with a base-URL swap. There is a documented Claude Code integration path if that is your harness. M3 is also routed through third-party aggregators such as OpenRouter.
• MiniMax Agent. The vendor's own hosted agent product, if you want to try the model's tool-use behaviour without writing an integration.
• Self-hosted. This is a serious hardware commitment. MiniMax's official deployment recipe baselines on 8 × NVIDIA B200, single node, tensor-parallel 8, with weights of ~444 GB at the pinned MXFP8 revision (the BF16 build on H200 is ~854 GB). SGLang is the supported framework, via a SHA-pinned Docker image. B300, GB300, GB200, H200 and AMD MI355X / MI350X / MI325X / MI300X configurations are also validated, all single-node.

MiniMax's docs include a warning worth repeating, because it trips people up constantly: "'23B activated parameters' does not mean that the deployment only needs memory for 23B parameters." The full 428B of weights must be resident or sharded. Sparse activation buys you compute, not VRAM.

For quantised community builds, memory maths and consumer-hardware options, see our walkthrough on running MiniMax M3 locally. For API wiring, agent harness setup and an evaluation playbook, the MiniMax M3 developer guide covers the integration path end to end.

How does MiniMax M3 compare to DeepSeek V4, GLM, Qwen and Kimi?

Using the independent numbers above rather than launch-day marketing, the open-weights picture at the end of August 2026 looks like this:

• vs DeepSeek V4 Pro 0813 — DeepSeek leads on aggregate intelligence (53 vs 45) at a higher blended price ($0.70 vs $0.20). Pick DeepSeek for raw quality per task; pick M3 when volume economics or context length dominate.
• vs GLM-5.3 — the strongest all-round case against M3 right now. GLM-5.3 (max) scores 60 at $0.90 blended, and GLM-5.3-Flash scores 57 at $0.10 — cheaper and substantially smarter than M3 on this index. If your workload fits inside a normal context window, Flash is hard to argue with. We put the two coding lines head to head in GLM vs MiniMax M3 for coding.
• vs Qwen3.8 — Qwen's 2.4T A95B build scores 58, and even the 27B scores 52. Qwen wins on quality and on ecosystem breadth; M3 wins on price and on context.
• vs Kimi K3 — Kimi K3 (max) ties for the top open-weights score at 60, but at $2.30 blended it is over ten times M3's cost per token. Different budget bracket entirely.

The defensible claim for M3 today is narrow but real: it is the cheapest credible open-weights agentic model, and it is the only one in that group with a million-token window. If your bottleneck is fitting an entire codebase or a long document set into one prompt, nothing on that list substitutes for it. If your bottleneck is answer quality per task, several models now beat it. For a broader cross-model view, see our DeepSeek V4 vs Qwen, Kimi and MiniMax comparison.

Should you build on MiniMax M3 today?

A decision rule rather than a recommendation:

• You need more than 256K of context. Use M3. Nothing else in the open-weights tier offers 1M tokens, and the MSA architecture is what makes it affordable rather than theoretical. Just budget for the 512K price cliff.
• You are optimising cost per token at high volume. M3 at a blended $0.20/M is the cheapest option in its intelligence band. Benchmark it against GLM-5.3-Flash at $0.10 before committing — Flash is cheaper and scores higher, but caps out well below 1M context.
• You are optimising answer quality. M3 is not the pick in August 2026. Kimi K3, GLM-5.3, Qwen3.8 and DeepSeek V4 Pro all score higher on independent aggregate evaluation.
• You are still on M2.7. No urgency. M2.7 remains on the API at identical pricing and is not deprecated. Migrate when you need the context window or the multimodal input, not on principle.
• You are waiting for M3.1. Do not plan around it. Nothing has been announced, and MiniMax's engineering attention this summer has visibly been on H3 and Music 3.

Whatever you pick, keep the inference layer model-agnostic. The three months between M3's launch and today produced four open-weights models that outscore it. That cadence is not slowing down, and the teams that handle it well are the ones for whom a model swap is a config change rather than a refactor. If you need that kind of evaluation and self-hosting experience on your team, Codersera matches you with vetted remote developers who have shipped it in production.

FAQ

When was MiniMax M3 released?

MiniMax M3 was announced on 1 June 2026, on the evening of 31 May US Eastern time. The open weights were published to the MiniMaxAI/MiniMax-M3 Hugging Face repository on 2 June 2026 at 07:49 UTC, alongside the quantised MiniMax-M3-MXFP8 build. The MiniMax Sparse Attention paper describing the architecture followed on 11 June 2026.

Is there a MiniMax M3 Pro?

No. As of 31 August 2026 there is no MiniMax M3 Pro on Hugging Face, in the MiniMax API model list, or on the MiniMax product site. The only official M3 variant is MiniMax-M3-MXFP8, which is the same model quantised to MXFP8 precision for self-hosting — not a higher-capability tier. Anyone quoting M3 Pro specifications is describing something that has not been released.

Is MiniMax M3.1 out?

No. There is no M3.1 as of 31 August 2026. MiniMax has shipped point releases before — M2 was followed by M2.1, M2.5 and M2.7 — so an M3.1 is plausible on precedent, but nothing has been announced and no repository exists. The M3 repo itself was last updated on 23 July 2026 with documentation changes, not a version bump.

Is MiniMax M3 open source?

M3 is open weights, not open source in the OSI sense. The full 428B weights are downloadable from Hugging Face, but they ship under MiniMax's own minimax-community licence rather than MIT or Apache 2.0. Read the licence file before commercial deployment. Training data and training code are not published.

How much does MiniMax M3 cost?

API pricing is $0.30 per million input tokens and $1.20 per million output tokens for prompts up to 512K, doubling to $0.60 / $2.40 above 512K. Cached reads are $0.06 per million. A priority tier costs 1.5× standard. Flat-rate coding subscriptions run $22/month (Plus), $55/month (Max) and $132/month (Ultra).

How does MiniMax M3 compare to DeepSeek V4?

DeepSeek V4 Pro 0813 scores 53 on the Artificial Analysis Intelligence Index against M3's 45, so DeepSeek is meaningfully stronger on aggregate reasoning and coding quality. M3 is roughly three times cheaper at a blended $0.20 per million tokens versus $0.70, and offers a 1M-token context window. Choose DeepSeek for quality, M3 for cost and context length.

What hardware do you need to self-host MiniMax M3?

MiniMax's reference deployment is 8 × NVIDIA B200 in a single node at tensor-parallel 8, using SGLang and the MXFP8 weights, which occupy roughly 444 GB. The BF16 build on H200 is about 854 GB. B300, GB300, GB200 and AMD MI355X / MI350X / MI325X / MI300X are also validated. The 23B active-parameter figure describes compute, not memory — all 428B parameters must be resident.

Is MiniMax H3 a newer version of M3?

No. H3, published on 28 July 2026, is a video generation model — a dense 33B-parameter omni-modal transformer that produces video with synchronised stereo audio at up to 2K resolution in 4–15 second clips. It is a separate product line with its own licence. If you are choosing a model for text, coding or agent work, H3 is not a candidate; M3 remains MiniMax's current language model.