GPT-5.6 Sol vs MiniMax M3: Top Intelligence vs Rock-Bottom Price (2026)

GPT-5.6 Sol and MiniMax M3 are the two large language models compared here, and they sit at opposite corners of the 2026 frontier. GPT-5.6 Sol is OpenAI's top-capability tier, generally available July 9, 2026, priced at 5 dollars per million input tokens and 30 dollars per million output tokens. MiniMax M3 is MiniMax's open-weight flagship, released June 1, 2026, priced at 0.30 dollars input and 1.20 dollars output per million tokens on contexts up to 512K, roughly 16 times cheaper on input and about 25 times cheaper on output. On the one independent evaluator that scores both models the same way, Artificial Analysis, GPT-5.6 Sol leads the Intelligence Index 59 to 44. MiniMax M3 answers with a downloadable, self-hostable mixture-of-experts model, a 1,000,000-token context, and native text, image, and video input. This is a split verdict, not a single winner. Best for peak measured intelligence and the OpenAI ecosystem: GPT-5.6 Sol. Best for price, open weights, and native multimodality: MiniMax M3.

Quick Verdict

This is a split verdict by use case, not a single overall winner. We ran both models side-by-side through their APIs, pulled the pricing directly from each vendor's own pages, and added our own hands-on notes from using both on reasoning and coding prompts. We have not run weeks of controlled, identical-task benchmarking of the two head-to-head, so where we lean on numbers we attribute them to their source, and where a figure is a vendor's own we label it. The honest summary is that these two models are not really chasing the same buyer. Here is the short version.

• Best for peak measured intelligence: GPT-5.6 Sol. On the Artificial Analysis Intelligence Index version 4.1 — the one composite that scores both models with the same battery — Sol sits at 59 while MiniMax M3 scores 44, a clear 15-point lead.
• Best for price: MiniMax M3, and it is not close. At standard rates its output at 1.20 dollars per million tokens is about 25 times cheaper than Sol at 30 dollars, and its input at 0.30 dollars is roughly 16 times cheaper than Sol at 5 dollars.
• Best for open weights and self-hosting: MiniMax M3. It ships a downloadable open-weight mixture-of-experts model you run on your own hardware for full data sovereignty. GPT-5.6 Sol is closed and API-only.
• Best for measured coding: GPT-5.6 Sol. It ranks first on the Artificial Analysis Coding Agent Index at 80, the top charted independent score, and MiniMax M3 is not placed on that leaderboard. Sol also ships a full agentic tool stack on by default.
• Best for native multimodality: MiniMax M3. It takes native text, image, and video input and supports computer use, where Sol reads text and image and returns text only.
• Best for ecosystem and compliance: GPT-5.6 Sol. US hosting with regional data-residency endpoints and the full OpenAI stack across ChatGPT, Codex, and the API, where MiniMax's hosted API runs in China, though its open weights let you self-host anywhere.

Bottom line: if you need the strongest measured model, the top charted coding score, or US-hosted compliance, pick GPT-5.6 Sol. If you are cost-constrained, want to own your weights, or need native video and self-hosting, MiniMax M3 gives you frontier-adjacent quality at a fraction of the price. We did not crown a single winner because the two models optimize for different things, and the numbers back both stories at once: a real capability gap and a much wider price gap. You can read the deeper single-model breakdowns in our GPT-5.6 Sol review and our MiniMax M3 review.

At a Glance

Before the detail, here is the side-by-side that frames everything below. All pricing in this table was fetched directly from each vendor's pricing page in July 2026 and is marked verified. Benchmark figures are attributed to their source; the independent numbers come from Artificial Analysis, and any vendor-reported figure is labeled as such in the benchmarks section.

Two patterns jump out. First, on the single independent yardstick that scores both, GPT-5.6 Sol is meaningfully ahead on intelligence, and it is the only one of the two with a charted independent coding score. Second, on price, MiniMax M3 is not a little cheaper but an order of magnitude cheaper, and it adds open weights and native video that a closed flagship cannot match. Everything below is really an argument about which of those two facts matters more for your workload.

Overview

GPT-5.6 Sol in one paragraph

GPT-5.6 Sol is the top-capability tier of OpenAI's GPT-5.6 family, generally available on July 9, 2026 across ChatGPT, Codex, and the API. It is built for the hardest reasoning, agentic, and coding work, with a reasoning-effort scale and new maximum and multi-agent modes for problems that reward extra compute. It carries a 1,050,000-token context window, accepts text and image input, and ships a complete agentic tool stack — function calling, web search, file search, code interpreter, computer use, and MCP — on by default. It is a closed model: no downloadable weights, no self-hosting, US-hosted with regional data-residency endpoints. On the independent Artificial Analysis Intelligence Index it leads its class, and it is the pricier of the two by a wide margin. The trade it offers is simple: the strongest measured model and the deepest ecosystem, at a premium price. Sol has two cheaper siblings worth knowing about — see our GPT-5.6 Terra and GPT-5.6 Luna pages if the flagship is more than you need.

MiniMax M3 in one paragraph

MiniMax M3 is the open-weight flagship from MiniMax, a Shanghai lab, released June 1, 2026. It is a mixture-of-experts model with 428 billion total parameters and roughly 23 billion active per token, using the lab's MiniMax Sparse Attention to keep long-context inference efficient. It is natively multimodal — text, image, and video input — supports computer use, and carries a 1,000,000-token context window. The headline is the price: 0.30 dollars input and 1.20 dollars output per million tokens at standard rates, among the lowest at this capability level. Because the weights are downloadable, you can self-host M3 in your own cloud or on-premise, which is the answer to data-residency worries about its China-hosted API. On the independent Artificial Analysis Intelligence Index it is one of the strongest open-weight models, tied with DeepSeek V4-Pro. The trade it offers is the mirror image of Sol's: frontier-adjacent quality, native multimodality, and ownership of your weights, at a fraction of the cost.

Pricing, Verified

We pulled both price sheets directly from each vendor in July 2026. GPT-5.6 Sol is usage-based on the OpenAI API: 5 dollars per million input tokens, 0.50 dollars per million cached input tokens, and 30 dollars per million output tokens, with a Batch API at half price. MiniMax M3 is dramatically lower and carries one wrinkle worth understanding.

MiniMax publishes a two-tier structure keyed to context length. For any request whose context stays at or below 512K tokens, input is 0.30 dollars and output is 1.20 dollars per million. For requests that push above 512K tokens toward the one-million-token ceiling, both rates double, to 0.60 dollars input and 2.40 dollars output per million. This matters if you routinely stuff the full window with long documents or large agent traces: your effective rate is the higher tier, not the headline one. We flag it because it is easy to miss and it changes budgeting for long-context jobs.

Even with the long-context surcharge, MiniMax stays far cheaper. At standard rates it is roughly 16 times cheaper on input and about 25 times cheaper on output than Sol. At the doubled long-context tier it is still around 8 times cheaper on input and about 12 times cheaper on output. Sol's counter is that its pricing is flat: there is no context-length surcharge, so a full-window request costs the same per token as a short one. In absolute terms, though, Sol remains the expensive option at any context length. If per-token spend is your binding constraint, this section decides the comparison on its own.

Benchmarks and What They Mean

The one apples-to-apples signal we trust for both models is the Artificial Analysis Intelligence Index version 4.1, because a single third party runs the same battery on each. On that index, GPT-5.6 Sol scores 59 and MiniMax M3 scores 44. That is a 15-point gap, and it is the clearest single number in this comparison: MiniMax M3 is a strong model, but Sol is measurably ahead on general reasoning and problem-solving.

It helps to put the 44 in context. Among open-weight models in mid-2026, that score ties MiniMax M3 with DeepSeek V4-Pro at the front of the open pack — no small feat for a downloadable model, and the reason M3 is a genuine alternative rather than a budget compromise. The gap to Sol is real, but so is the fact that M3 is playing in the frontier's neighborhood while giving away its weights.

On coding, the strongest independent evidence belongs to Sol. It ranks first on the Artificial Analysis Coding Agent Index at 80, the top charted score on that leaderboard, and MiniMax M3 is not placed on it at all. Combined with an agentic tool stack that is on by default, that makes Sol the safer pick when your success metric is measured coding performance under a third-party protocol. This is a result no open-weight rival has yet matched on that particular leaderboard.

MiniMax has its own coding story, and it is worth reporting carefully. MiniMax self-reports 59 percent on SWE-bench Pro, a figure it says beats GPT-5.5 and Gemini 3.1 Pro and approaches Claude Opus 4.7 on that specific test. We treat that as a vendor claim, not an independent result, because it comes from MiniMax's own harness rather than a neutral evaluator. One coincidence is worth flagging so it does not mislead anyone: that self-reported 59 percent on SWE-bench Pro is a coding pass rate from the vendor, and it is unrelated to Sol's independent Artificial Analysis Intelligence score, which happens to read 59 as well. The two numbers measure different things, come from different sources, and should never be added together or ranked against each other. For a like-for-like open-weight coding contrast, our GPT-5.6 Sol versus DeepSeek V4 comparison walks the same ground with a different challenger.

Architecture and Deployment

The two models are built on opposite philosophies, and it shows in what you can do with them. MiniMax M3 is an open-weight mixture-of-experts model: 428 billion total parameters with roughly 23 billion active per token, so it runs far more cheaply than a dense model of comparable size, and it uses MiniMax Sparse Attention to hold a million-token context without the usual quadratic blowup. Because the weights are downloadable, you can quantize, fine-tune, and deploy M3 inside your own environment, which is the whole point for teams with sovereignty requirements.

GPT-5.6 Sol does not disclose its architecture, and it does not need to for its intended buyer: you consume it as a hosted service through the OpenAI API, ChatGPT, and Codex, with the agentic tool stack, reasoning-effort control, and multi-agent modes handled for you. That is a strength if you want managed capability and a weakness if you need to own the model. The dividing line is deployment, not just performance: Sol is the model you rent at the top of the market, and M3 is the model you can own and run anywhere. If self-hosting is not a requirement, Sol's managed stack removes a lot of operational work; if it is a requirement, only M3 can satisfy it.

Economics at Scale

Price is where this comparison stops being close. Consider a workload that generates 100 million output tokens a month — a mid-sized production application. On GPT-5.6 Sol at 30 dollars per million, that is 3,000 dollars in output alone. On MiniMax M3 at 1.20 dollars per million, the same volume is 120 dollars. That is the 25-times gap made concrete, and it is before you add input tokens, where M3's roughly 16-times advantage compounds the savings. Self-hosting shifts the math again: you trade per-token API fees for fixed infrastructure cost, which can pay off at very high volume or when data cannot leave your walls.

The counter-argument is value density, and it is a real one. If a task is high-stakes and low-volume — a legal analysis, a hard refactor, a research synthesis where a wrong answer is expensive — then paying 30 dollars per million output tokens for the strongest measured model is a rounding error against the cost of being wrong. Sol's 15-point lead on the independent Intelligence Index is exactly the kind of edge that justifies a premium on that class of work. The practical pattern many teams land on is to route bulk, tolerant traffic to MiniMax M3 and reserve Sol for the hardest reasoning and coding, which is also the logic behind our best AI coding tools roundup.

How We Compared Them

Our method here is a mix of hands-on use and sourced research, and we want to be transparent about which is which. We ran both models through their APIs on the same reasoning and coding prompts to get a feel for tone, reliability, and tool behavior, and we pulled every price in this article directly from each vendor's own pricing pages in July 2026, then marked those figures verified. For capability, we did not run a controlled, weeks-long, identical-task benchmark ourselves; instead we lean on the Artificial Analysis Intelligence Index and Coding Agent Index because they apply one consistent protocol to both models. Where a number comes from a vendor's own testing — MiniMax's SWE-bench Pro figure, for example — we label it a vendor claim and keep it separate from the independent scores. That separation is deliberate: mixing an independent index with a self-reported benchmark is how misleading head-to-head charts get made, and we would rather under-claim than stack numbers that do not belong together.

Winner by Category

Because this is a split decision, the useful question is not which model wins overall but which wins for a given need. Here is how the categories fall.

• Measured intelligence: GPT-5.6 Sol, 59 to 44 on the independent Intelligence Index.
• Independent coding: GPT-5.6 Sol, ranked first on the AA Coding Agent Index where M3 is not charted.
• Price: MiniMax M3, roughly 16 times cheaper on input and about 25 times cheaper on output.
• Open weights and self-hosting: MiniMax M3, the only one you can download and run yourself.
• Native multimodality: MiniMax M3, with text, image, and video input plus computer use.
• Context window: GPT-5.6 Sol by a hair, 1,050,000 versus 1,000,000 tokens — effectively a tie.
• Ecosystem and compliance: GPT-5.6 Sol, US-hosted with the full OpenAI agentic stack and regional data residency.
• Long-context cost predictability: GPT-5.6 Sol, whose flat pricing has no context-length surcharge — though M3 stays cheaper in absolute terms even so.

Pros and Cons

When to Pick Each

When to pick GPT-5.6 Sol

Pick GPT-5.6 Sol when capability and integration matter more than per-token cost. If you are doing serious agentic coding, multi-step reasoning, or computer-use automation on the OpenAI stack, it is the stronger model on both independent indices, and its tool stack, image input, and reasoning-effort control give you leverage MiniMax M3 does not match out of the box. Pick it if you are a Western enterprise with data-residency or compliance obligations, because US hosting and regional residency endpoints clear bars a China-hosted API cannot. And pick it when your workload is moderate in volume but high in value, where paying 30 dollars per million output tokens for the best answer is trivial against the cost of a wrong one. If you already live inside ChatGPT, Codex, or the Responses API, Sol is the natural default, and our GPT-5.6 Sol versus Claude Sonnet 5 comparison covers the closest premium alternative.

When to pick MiniMax M3

Pick MiniMax M3 when cost, control, or multimodality dominate. If you are running high-volume inference where token spend is the binding constraint, a roughly 25-times-cheaper output rate changes what is economically viable, turning workloads that are unaffordable on Sol into routine ones. Pick it if you need to own your weights: the open-weight release lets you self-host, fine-tune, and keep data inside your jurisdiction, with the China-hosted API as a convenience rather than the only option. Pick it if your work involves native video input or computer-use agents, where M3's multimodality covers more ground than Sol. You give up a measurable slice of frontier capability and the charted independent coding score, and you take on the operational work of self-hosting if compliance requires it — but you get most of the quality, more input modalities, and a fraction of the price. If you are weighing M3 against other open-weight options, our DeepSeek V4 review covers its closest rival on the Intelligence Index.

Final Verdict

This is a split verdict by use case, tilted toward GPT-5.6 Sol on capability and toward MiniMax M3 on price and openness. On the one independent signal that scores both models the same way, the Artificial Analysis Intelligence Index version 4.1, Sol leads 59 to 44, and it is the only one of the two with a charted independent coding score, ranking first on the AA Coding Agent Index. It is the stronger measured model, the more deeply integrated one for agentic work, and the only one that clears Western data-residency requirements through OpenAI hosting. If your priority is peak capability, charted coding, or compliance, Sol is the pick, premium price and all.

MiniMax M3 wins the other half of the argument decisively. It is roughly 16 times cheaper on input and about 25 times cheaper on output at standard rates, it ships open weights you can download and self-host, it matches Sol's context window within a rounding error, and it reads native video that Sol cannot. Its 44 on the independent Intelligence Index ties it with DeepSeek V4-Pro at the front of the open-weight field, and its self-reported SWE-bench Pro result shows real coding strength even if it is not an independent number. If your priority is price, control, or multimodality, M3 is the pick, and it is not a compromise so much as a different bet. We did not name a single winner because these two models are optimized for different jobs, and both stories are true at the same time: a genuine capability gap and a much larger price gap. Choose the one whose gap you can least afford.

Frequently Asked Questions

Is GPT-5.6 Sol better than MiniMax M3?

On measured capability, yes. GPT-5.6 Sol tops the Artificial Analysis Intelligence Index at 59 versus 44 for MiniMax M3, and it ranks first on the independent AA Coding Agent Index, a charted score MiniMax M3 does not have. But MiniMax M3 is roughly 25 times cheaper per output token, ships open weights you can self-host, and takes native image and video input, so the better model depends on whether you are optimizing for peak intelligence and ecosystem or for price, openness, and multimodality.

How much cheaper is MiniMax M3 than GPT-5.6 Sol?

A lot. At standard rates, MiniMax M3 output at 1.20 dollars per million tokens is about 25 times cheaper than GPT-5.6 Sol at 30 dollars, and MiniMax input at 0.30 dollars is roughly 16 times cheaper than Sol at 5 dollars. Even at MiniMax’s doubled long-context rate above 512K tokens, it stays around 8 times cheaper on input and about 12 times cheaper on output. All prices were taken directly from each vendor’s own pages in July 2026.

Why do MiniMax M3’s prices double above 512K tokens?

MiniMax publishes a two-tier structure. For requests whose context stays at or below 512K tokens, input is 0.30 dollars and output is 1.20 dollars per million. For requests that push above 512K tokens toward the one-million-token ceiling, both rates double to 0.60 dollars input and 2.40 dollars output. Long documents and large agent traces cost more per token, so budget for the higher tier when you routinely run near the top of the context window.

Is MiniMax M3 open source?

It is open weight. MiniMax M3 ships a downloadable model you can run on your own hardware, which is what makes self-hosting and full data residency possible. As with most open-weight releases, that is not the same as fully open source: the weights are published, but the complete training code and data recipe are not, so you can deploy and fine-tune the model without being able to reproduce the training run from scratch. GPT-5.6 Sol, by contrast, is closed and API-only.

Which has the bigger context window, GPT-5.6 Sol or MiniMax M3?

They are within a rounding error of each other. GPT-5.6 Sol carries a 1,050,000-token context window, and MiniMax M3 carries 1,000,000 tokens. Both comfortably hold large codebases, long documents, or extended agent histories in a single prompt, so context length is effectively a tie and should rarely be the deciding factor between them.

Is MiniMax M3’s 59 percent on SWE-bench Pro comparable to Sol’s scores?

No, and it is important not to stack the numbers. MiniMax’s 59 percent on SWE-bench Pro is a vendor self-reported pass rate measured on MiniMax’s own harness. Sol’s coding evidence comes from Artificial Analysis, a third-party evaluator, on a different index and a different protocol. They are different benchmarks under different regimes, so we report MiniMax’s figure as a vendor claim and never rank it head-to-head against Sol’s independent number.

Can you self-host GPT-5.6 Sol?

No. GPT-5.6 Sol is a closed model available only through OpenAI’s API, ChatGPT, and Codex. There are no downloadable weights, so there is no self-hosting or on-premise option. If self-hosting for data sovereignty is a hard requirement, MiniMax M3’s open weights are the reason to look its way, since you can run the model inside your own environment.

Which model is better for coding?

Sol has the stronger independent evidence. It ranks first on the Artificial Analysis Coding Agent Index at 80, a charted independent score, where MiniMax M3 does not appear on the leaderboard at all. That is the number we weight most heavily, because it is measured by a third party under a fixed protocol and a full agentic tool stack ships on by default. MiniMax’s own camp points to a self-reported 59 percent on SWE-bench Pro, which it says beats GPT-5.5 and Gemini 3.1 Pro on that specific test; we report it as a vendor figure and do not rank it directly against the independent index.

Does MiniMax M3 support images and video?

Yes. MiniMax M3 is natively multimodal, accepting text, image, and video input, and it supports computer use for agentic workflows. That is broader input coverage than GPT-5.6 Sol, which takes text and image in but not video. If your workload involves reading video frames or driving a screen, MiniMax M3 covers more of it out of the box; if you mainly need text and image reasoning, both models handle it.

Is MiniMax M3 safe for enterprise and data residency?

It depends on how you deploy it. MiniMax’s hosted API runs in China, which many US and EU regulated buyers cannot use for sensitive data. The escape hatch is the open weights: because you can self-host MiniMax M3 in your own cloud or on-premise, you can keep data inside your jurisdiction. GPT-5.6 Sol takes the opposite route, offering US hosting with regional data-residency endpoints but no self-hosting at all.

Which should I choose for high-volume production?

For pure token volume where cost is the binding constraint, MiniMax M3 changes the math: at roughly 25 times cheaper per output token, workloads that are uneconomical on Sol become routine. For high-value, moderate-volume work where a wrong answer is expensive and engineer time dominates, Sol’s 15-point lead on the independent Intelligence Index can justify the premium. Many teams run both, routing bulk traffic to MiniMax M3 and hard reasoning or coding tasks to Sol.

What is the final verdict, GPT-5.6 Sol or MiniMax M3?

It is a split decision, because the two models optimize for different things. GPT-5.6 Sol is the stronger measured model and the deeper ecosystem, and it clears Western compliance bars MiniMax’s hosted API cannot. MiniMax M3 is far cheaper, open-weight and self-hostable, and natively multimodal including video. Choose Sol for peak capability and compliance; choose MiniMax M3 for price, control, and multimodality. Neither is a mistake, and the numbers back both stories at once.