MiniMax M3 vs Hunyuan 4: Which Is Better for Coding and Agents?

MiniMax M3 vs Hunyuan 4: Quick Verdict

If you already know which trade-off matters, you can examine the live MiniMax M3 API and Hy4 Preview API pages. If not, the differences below are the ones that will change an actual implementation decision.

MiniMax M3 vs Hunyuan 4: Key Differences

First, the name. “Hunyuan 4” and “Hunyuan4” are common search terms for the model Tencent officially calls Hy4 Preview. API requests must use the provider's exact model identifier rather than the informal search name.

The architecture details come from the MiniMax M3 model card and Tencent Hy4 Preview model card. They explain how the models are built, not which one will finish your issue ticket correctly. A larger parameter count is not an API quality score, and a 1M context limit does not guarantee reliable retrieval across every position in a 1M-token prompt.

There is also a route-specific detail that other comparisons may miss. MiniMax describes M3 as natively multimodal with image and video input, but the current GPT Proto route lists text, image, and document input. Hy4 Preview currently accepts text and returns text. For an API buyer, the route you can actually call matters more than a modality shown in a general model announcement.

Architecture and Reasoning: What Changes in Practice?

MiniMax M3 favors control and multimodal context

M3 activates roughly 23B of its approximately 428B parameters for each token. Its defining change is MiniMax Sparse Attention, or MSA, which selects relevant key-value blocks rather than applying full attention to every token. MiniMax reports that at a 1M-token context, MSA cuts per-token compute to about one-twentieth of its previous-generation approach, with more than 9× faster prefill and 15× faster decoding. Those are vendor measurements, not a Hy4 API speed test, but they explain why MiniMax built M3 around long sessions rather than short chat alone. The M3 launch report provides the methodology and caveats.

The practical advantage is flexibility. M3 supports enabled, adaptive, and disabled thinking modes. A coding agent can spend more reasoning on an architectural change, switch to adaptive reasoning for mixed work, or avoid a long reasoning trace for formatting and extraction. That control does not make poor prompts disappear. It does make it easier to align model effort with the task's value.

M3's image input is equally concrete. A developer can send a screenshot, a rendered error state, or a design reference alongside written requirements. That shortens the gap between “the page is wrong” and a model seeing what is wrong.

Hy4 Preview puts more capacity behind text reasoning

Hy4 has a 770B-parameter backbone with 49B parameters active per token. Its 78-layer design contains 256 routed experts and one shared expert in each MoE layer, with eight routed experts selected per token. Tencent also adds a native multi-token-prediction layer for speculative decoding. Gated DSA and IndexCache are intended to make its 1M context more practical to serve.

Hy4 defaults to high reasoning and offers a no_think mode for direct responses. That sounds similar to M3's reasoning switch, but the release-stage warning is different. Tencent openly states that the preview can spend longer than necessary reasoning through complex tasks and can over-verify its own work. For hard debugging, another verification pass may help. For a short extraction job repeated 50,000 times, it may simply add latency and tokens.

The takeaway is straightforward: M3 gives you a broader input surface and finer reasoning choices; Hy4 aims more capacity at difficult text work. Neither design removes the need to measure task completion, latency, and token use in your own agent loop.

MiniMax M3 vs Hunyuan 4 for Coding

Hy4 has the stronger public benchmark profile on the tests that appear in both model cards.

Sources: the MiniMax M3 model card and Tencent Hy4 Preview model card. Both groups report their own evaluation results.

The first honest reading is that Hy4 leads all three. The second, equally important reading is that these are not controlled head-to-head results.

MiniMax says its SWE-bench Pro result was produced on internal infrastructure with Claude Code as the scaffold. Terminal-Bench 2.1 used a different scaffold and a sandbox with its own compute, timeout, and output-token settings. Tencent publishes Hy4's scores in its model card, but the two vendors did not jointly run both models under one protocol. A scaffold can decide which files enter context, which commands a model may run, how retries work, and when a task stops. Change the scaffold and you are no longer measuring only the model.

My judgment: Hy4 is the better candidate if your selection process begins with public coding benchmarks, but not the automatic production winner. Put it against M3 on the same repository snapshot, system prompt, tool definitions, timeout, and pass/fail tests. Until it wins there, the benchmark gap is a reason to evaluate Hy4—not a reason to migrate blindly.

M3 remains easier to justify when visual debugging, lower generation cost, and a non-preview release matter alongside coding scores.

Which Is Better for AI Agents?

Agent workloads make price and failure behavior more important than they look in a single chat. One user request can trigger planning, repository search, tool calls, error recovery, a new plan, more tool calls, and a final answer. A model that generates 20% more text per step can compound that difference across the loop.

MiniMax has published unusually long internal M3 case studies. In one, the model worked for nearly 12 hours on a paper-reproduction task, making 18 commits and producing 23 figures. In another, it spent about 24 hours optimizing CUDA kernels through 1,959 tool calls and 147 submissions; MiniMax reported a 9.4× speedup in the resulting kernel. These are useful signs that M3 was designed for sustained work. They are still vendor-run cases, not independent proof that every M3 agent will remain reliable for 24 hours.

Hy4's case rests more heavily on its benchmark results and Tencent's internal engineering evaluation. Tencent reports that 163 internal experts reviewed 203 engineering tasks, giving Hy4 an average score of 2.99 out of 4. That evaluation placed it slightly above GLM-5.3 and Kimi K3, but it did not include M3. The Tencent model card also positions Hy4 around planning, debugging, verification, game development, office work, and research.

For a cost-sensitive production agent, I would start with M3. Its lower output rate limits the cost of long reasoning and retry loops, and adaptive thinking gives the application another control. For a difficult text-only software task where completion matters more than token spend, Hy4 deserves a trial. Watch generated-token volume and repeated verification closely.

The agent framework still owns permissions, tool execution, timeouts, retries, and acceptance tests. Access to an agent-focused model does not turn the API gateway itself into an autonomous multi-agent system.

MiniMax M3 vs Hunyuan 4 for Frontend Coding

“Frontend coding” hides two different jobs. The first begins with pixels: reproduce this screenshot, diagnose this broken mobile layout, or update a component to match a design reference. The second begins with text: implement this component specification, fix these failing tests, or refactor this repository.

For the first job, M3 is the better fit. GPT Proto's M3 route accepts image input, while Hy4 Preview is text-to-text. Giving the model the screenshot removes a translation step in which a human must describe spacing, hierarchy, alignment, clipping, or color problems in words.

For text-specification-to-code work, Hy4 may have the higher ceiling. Tencent specifically reports improvements in frontend implementation, visual taste, and interaction quality. That is a vendor claim, so it should become a test hypothesis: give both models the same requirements, browser tools, time limit, and automated checks.

A community Hy4 frontend test illustrates why the acceptance criteria matter. In a FlappyBench discussion, the author favored Hy4's distinctive interface and gameplay. Commenters challenged the conclusion because the complete prompt was not provided, and some preferred a competing result for matching the original game's difficulty more closely. The thread did not test M3. Its value here is methodological: attractive screenshots, prompt compliance, gameplay behavior, responsiveness, and maintainable code are different criteria.

So the practical verdict is narrower than “Model A makes prettier websites.” Choose M3 for screenshot-driven iteration. Test Hy4 for well-specified repository work where you can score functionality and code quality separately.

MiniMax M3 vs Hunyuan 4 Pricing

The live GPT Proto rates favor M3 on normal input and output, while Hy4 has the cheaper cache read.

Sources: live GPT Proto pricing for MiniMax M3 and Hy4 Preview, checked September 10, 2026.

Price per million tokens is abstract, so consider a repository task with 100,000 input tokens and 10,000 generated tokens:

These estimates multiply the displayed per-token rates; they exclude retries, cache writes not shown on the Hy4 page, tool execution, and any extra agent steps. Actual cost therefore depends on how much each model generates before completing the task.

M3 is the cost-effective choice for ordinary coding and output-heavy agents. Hy4's cache-read price is attractive when a large stable prefix is reused repeatedly, but its output price is $2.376 per million tokens versus $0.96 for M3. In the two examples above, M3 still costs less.

There is one pricing trap worth avoiding. MiniMax's direct API uses context-length and service-tier pricing, including a discounted standard tier. That makes a single “GPT Proto is X% cheaper than official” claim fragile. For this comparison, the cleanest approach is to compare the two live GPT Proto routes in absolute dollars and recheck them before publication.

Speed, Context, and Production Readiness

There is not enough comparable public data to name a speed winner. MiniMax's MSA figures compare its sparse-attention implementation with earlier approaches. Tencent's architecture and inference claims use its own stack. Neither is a same-region API test measuring time to first token, output speed, or completion time for M3 and Hy4 under matched load.

The 1M context label needs the same restraint. It tells you the nominal window, not whether the model will retrieve a small but decisive fact buried near the middle of a giant repository prompt. For long-context work, evaluate answer accuracy at multiple document positions and compare it with a retrieval-based workflow. Shoving an entire repository into one request may be convenient, but it can also increase latency and distract the model with irrelevant files.

Release status supplies a clearer production signal. M3 is a full release. Hy4 is an early preview whose own model card documents unnecessary reasoning and over-verification. That does not make Hy4 unsuitable for production; it makes the burden of testing higher.

For a default production route today, I would choose M3. For Hy4, begin with shadow traffic or a bounded evaluation set, then promote it only if task success offsets its extra token cost and preview behavior.

License and Self-Hosting

Hy4 is the clearer choice for teams that care about an established permissive license. Tencent publishes its weights under Apache 2.0. MiniMax distributes M3 under the MiniMax Community License, so teams planning redistribution or commercial self-hosting should read those terms rather than assuming all “open-weight” releases grant the same rights.

The trade-off is infrastructure. Tencent's reference FP8 deployment uses tensor parallelism across eight GPUs plus model-specific settings for sparse attention, speculative decoding, reasoning parsing, and tool calls. Apache 2.0 removes one kind of friction; it does not make serving a 770B backbone inexpensive.

For most API developers, the license difference will matter less than quality, latency, and cost. For a company that must operate weights inside its own environment, it can decide the comparison by itself: Hy4 has the cleaner licensing path, provided the company can support the serving stack.

Which Model Should You Choose?

MiniMax M3 wins this comparison for most developers. Its advantage is not that it tops every benchmark—it does not. The case for M3 is that price, multimodal input, reasoning control, and release maturity form the better package for day-to-day API work.

Hy4 Preview is not a weaker copy. It is a more specialized bet. Its published coding and agent results are stronger, its Apache 2.0 license is easier to work with, and its text reasoning deserves a serious evaluation. The costs are higher output pricing, text-only input, substantial self-hosting requirements, and preview-stage behavior that Tencent has not hidden.

How to Access MiniMax M3 and Hy4 With One API Key

Both models use GPT Proto's OpenAI-compatible chat-completions endpoint. Create an API key, store it in an environment variable, and change only the model ID when you want to compare them.

export GPTPROTO_API_KEY="your-api-key-here"

Call MiniMax M3:

curl --request POST "https://gptproto.com/v1/chat/completions" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "MiniMax-M3",
    "messages": [
      {
        "role": "user",
        "content": "Review this JavaScript function for async race conditions and return a corrected version."
      }
    ],
    "stream": false
  }'

Run the same task with Hy4 Preview:

curl --request POST "https://gptproto.com/v1/chat/completions" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "hy4-preview",
    "messages": [
      {
        "role": "user",
        "content": "Review this JavaScript function for async race conditions and return a corrected version."
      }
    ],
    "stream": false
  }'

Using one endpoint and balance makes a controlled application-level comparison easier: keep the prompt, tools, timeout, and checks fixed, then record task success, total tokens, latency, and cost. GPT Proto supplies access to both models; your application or agent framework still handles orchestration and tool permissions.

You can start from the MiniMax M3 model page, the Hy4 Preview model page, or browse the full GPT Proto model catalog.