Mistral Large 4 vs Hy4 preview: which should you choose in 2026?
Mistral Large 4 (by Mistral AI) and Hy4 preview (by Tencent) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
Hy4 preview has the larger context window (1,048,576 tokens vs 524,288 tokens).
Choose Mistral Large 4 if…
- • you need image inputs
Choose Hy4 preview if…
- • you work with longer documents, transcripts, or codebases
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Mistral Large 4 | Hy4 preview |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
| Arena preference scores | ||
Human preference score for code and web development | — | 1633 ±9Preliminary |
Benchmark sources
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Oct 5, 2026.
Pricing, capabilities, and model facts
| Feature | Mistral Large 4 | Hy4 preview |
|---|---|---|
| Context & model facts | ||
| Developer | Mistral AI | Tencent |
| API provider | Mistral | Tencent |
| Input context | 524,288 tokens | 1,048,576 tokens |
| Maximum output | 262,144 tokens | 64,000 tokens |
| Released | — | — |
| Added to Writingmate | Oct 6, 2026 | Aug 28, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text, Image | Text |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | No |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Base input (per 1M tokens) | $0.68 | $0.834 |
| Base output (per 1M tokens) | $2.09 | $2.50 |
| Higher-context pricing tiers | Base rate only | Base rate only |
| Price-comparison caveat | At least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like. | |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Oct 6, 2026.
Why Pay for Multiple Subscriptions?
Comparing Mistral Large 4 from Mistral AI with Hy4 preview from Tencent? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Mistral Large 4 | Hy4 preview | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Mistral Large 4 vs Hy4 preview FAQ
Which is better, Mistral Large 4 or Hy4 preview?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. Hy4 preview has the larger context window (1,048,576 tokens vs 524,288 tokens).
Which model is cheaper to use through an API?
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
Which model supports more context?
Hy4 preview has the larger context window (1,048,576 tokens vs 524,288 tokens).
Can I switch between Mistral Large 4 and Hy4 preview?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
Related comparisons
More Mistral Large 4 comparisons
GPT-6.1 Sol Pro
OpenAI
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.
GPT-6.1 Sol
OpenAI
GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series.
Claude Sonnet 5.5
Anthropic
Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade.
Jev Router
Typesafe
Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost.
Perceptron Mk1.5
Perceptron
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents.
Ember-1
Fireworks
Ember-1 is a specialized reasoning model from Fireworks Research, built on Kimi K3.
More Hy4 preview comparisons
Ling 3.1 Flash
inclusionAI
Ling 3.1 Flash is a hybrid reasoning mixture-of-experts model from inclusionAI, with 25B active parameters out of 560B total.
GPT-6.1 Sol Pro
OpenAI
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.
GPT-6.1 Sol
OpenAI
GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series.
Claude Sonnet 5.5
Anthropic
Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade.
Jev Router
Typesafe
Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost.
Perceptron Mk1.5
Perceptron
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents.