Europe’s Most Powerful Open-Weight Model Is Here? 5 Sets of Data Show How Mistral Large 4 Stacks Up Against DeepSeek and GLM

Author’s note: Based on public data available on October 29, 2026, this article examines the real gap between Mistral Large 4, DeepSeek, and GLM across five dimensions.

On October 29, 2026, French AI company Mistral released improved Large 4 weights and announced official API pricing. In the Intelligence Index v5.0 update published by Artificial Analysis that same day, Mistral Large 4 scored 26 points, making it the highest-scoring open-weight model from outside China. But it still trailed DeepSeek V4.1 Flash 1017 and GLM-5.3 by 6 points, both of which scored 32.

This isn’t a promotional piece about a European model’s comeback. “Europe’s strongest” comes with clear caveats: Large 4 is just 1 point ahead of the runner-up, Meta Muse Spark Open, and seven open-weight models worldwide have higher overall scores. What’s worth watching is how close it has come to the leading models on coding agent benchmarks, while using significantly fewer tokens.

The takeaway: In three minutes, you’ll see where Large 4 has caught up, where it still falls short, and how developers and businesses can decide whether it’s worth testing.

Mistral Large 4 at a Glance

First, it’s important to get the timeline right: Large 4 didn’t make its debut on October 29. Mistral released a public preview on October 6, then spent about three more weeks on reinforcement learning and incorporated testing feedback from enterprise and cybersecurity partners. It released the improved model’s weights on October 29.

What Mistral Large 4’s Trillion Parameters Mean

The MoE (Mixture of Experts) architecture routes each token through only some of its experts. The model has 128 routed experts, selects 4 at a time, and also uses 1 shared expert. So, although it has approximately 1.05 trillion parameters in total, only about 49 billion are activated per token. Including the embedding and output layers, that figure rises to approximately 52 billion.

Keep in mind that fewer activated parameters don’t mean a smaller deployment memory footprint. Running the model still requires storing the full model and its runtime state, so the claim that it “only needs 52 GB of VRAM” is incorrect.

What’s Improved in Mistral Large 4

The improved version’s main gains are in efficiency, not overall scores. In Artificial Analysis evaluations using the same version of the index, the preview scored 25 and the improved version scored 26—an increase of just 1 point. An older version of the index gave the preview a score of 38 in early October, but that used a different evaluation framework from v5. You can’t use those scores to conclude that the model’s capabilities declined.

• Token efficiency: Average output per Intelligence Index task fell from approximately 135,000 tokens to 82,000, a drop of about 39%.
• Evaluation task cost: Cost fell from approximately $5.54 to $2.06, a drop of about 63%. That doesn’t mean every production task will see the same savings.
• Factuality: The hallucination rate on AA-Omniscience v1.1 fell from 41% to 28%. That’s a metric defined by this particular test set, not the probability of an error in everyday responses.

How Does Mistral Large 4 Compare With DeepSeek and GLM?

The table below summarizes independent evaluation data from Artificial Analysis, comparing Large 4 High, DeepSeek V4.1 Flash 1017 Max, and GLM-5.3 Max. These scores are a snapshot from October 29–30 and may change over time.

The Intelligence Index isn’t an accuracy score or a universal measure of a model’s intelligence. Version 5.0 consists of nine evaluations covering knowledge work, software engineering, automation, scientific reasoning, and other areas. The six-point gap shows that Large 4 hasn’t yet caught up with leading Chinese open models under this framework. The AA-Briefcase Elo gap also points to weaknesses in some complex knowledge-work tasks.

Where Does Mistral Large 4’s Coding Agent Excel?

On the Coding Agent Index, Large 4 scores 48, just behind GLM-5.3’s 49 and two points ahead of DeepSeek. But it’s best not to read too much into a one- or two-point difference. Token usage is more informative: Large 4 uses about 47% fewer tokens than GLM-5.3 and about 49% fewer than DeepSeek.

That said, this test compares the full “model + agent framework” combination, and all three use different frameworks. Total tokens also include input, cached, and output tokens, so lower usage doesn’t necessarily mean a lower bill. You’ll need to factor in each model’s pricing to calculate actual costs.

Why Do Official and Independent Leaderboards Differ?

The scores published by Mistral differ noticeably from Artificial Analysis’s test results. When reading leaderboards like these, check the test configurations first—you can’t combine numbers from different sources into a single ranking.

The agent framework, reasoning configuration, runtime environment, and tool-calling settings could all contribute to these differences, but there’s not enough public information to isolate the effect of each factor. It’s fair to say that “Large 4 performs well in Mistral’s published configuration.” It’s not fair to say that “independent tests prove it leads on both benchmarks.”

The same caution applies to cybersecurity: Large 4 scored 51% on the Cyber Index, below DeepSeek’s 57% and MiMo-V2.6-Pro’s 56%. Although Mistral is investing heavily in this area, that doesn’t mean it’s the strongest model across all security tasks.

How to choose: Leaderboards can help narrow your options, but you should validate your final choice against your own codebase and task replays. You can use APIYI apiyi.com to switch between candidate models with the same OpenAI-compatible code, then record success rates and token usage.

How to Calculate Mistral Large 4 API Costs

Large 4’s official pricing is about half its early non-promotional rate ($1.36 for input and $4.18 for output per million tokens). But during the preview period, a roughly two-week promotion had already brought prices close to the official rate: about $0.68 for input and $2.09 for output. So it’s more accurate to say that “pricing is close to the preview promotional rate and significantly lower than the early non-promotional rate” than to say “prices were cut by 50% for the first time on October 29.”

Cost per task is a weighted average within the evaluation framework, not the bill for a single real-world task. Based on these figures, DeepSeek has a higher overall score and a lower cost; GLM has a higher overall score but also costs more. Large 4 doesn’t lead on both measures. Mistral may also charge an extra 10% for inference in the EU or US, and you’ll need to check Batch and Priority pricing separately.

Here’s an estimate based on a hypothetical workload: 10 million input tokens and 2 million output tokens per day. At standard pricing, the daily cost is $7.00 + $4.20 = $11.20. If 80% of input tokens are served from the cache, the cost drops to $1.40 + $0.56 + $4.20 = $6.16, a saving of about 45%. Your actual cache hit rate depends on your usage data, and reasoning tokens, tool calls, and retries after failures can also increase costs.

How to Call the Mistral Large 4 API

The official API model ID is mistral-large-4. If you already have a project using the OpenAI SDK, just update the base_url and model name to test it:

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"  # APIYI 统一接口
)

response = client.chat.completions.create(
    model="mistral-large-4",
    messages=[{"role": "user", "content": "检查这段代码可能存在的安全问题"}]
)
print(response.choices[0].message.content)

import openai

# Mistral 官方标准价，单位：美元/百万 Token
INPUT_PRICE = 0.70
OUTPUT_PRICE = 2.10

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

def ask_with_cost(prompt: str, model: str = "mistral-large-4") -> str:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=2000
    )
    usage = response.usage
    # 未计入缓存折扣，实际计费以账单为准
    cost = (
        usage.prompt_tokens * INPUT_PRICE
        + usage.completion_tokens * OUTPUT_PRICE
    ) / 1_000_000
    print(f"输入 {usage.prompt_tokens}，输出 {usage.completion_tokens}，估算 ${cost:.6f}")
    return response.choices[0].message.content

print(ask_with_cost("为这个函数设计三个边界测试用例"))

Quick start: Claim the free testing credit for new users at APIYI apiyi.com, then check whether your target model is available and confirm its model ID. Before going live, test the API with a few real tasks. Check the console for current platform pricing.

Cost optimization: When comparing models, don’t look at the unit price alone. Track the total cost of successfully completing a task. When using pay-as-you-go API calls through APIYI apiyi.com, record retries, output tokens, and task results separately for each model.

Impact of the Mistral Large 4 Release

Impact on Developers

• Another option for coding agents: Large 4’s coding index is close to GLM-5.3’s while using fewer tokens, making it worth testing for long-running automated workflows.
• DeepSeek remains worth comparing for cost-sensitive tasks: DeepSeek has lower unit prices and a significant difference between peak and off-peak pricing. For high-volume workloads, Large 4’s token efficiency isn’t the only factor to consider.
• Community opinion is still taking shape: Some Hacker News users think the new pricing warrants another test, while others report long reasoning times in the preview version. These are early individual reports, and some don’t refer to the improved version.

Impact on Self-Hosting Teams

Open weights don’t mean running a model locally is free. Unsloth estimates that 1-bit quantization requires around 256–270GB of total memory, 4-bit requires around 630GB, and 8-bit requires around 1.2TB. Long contexts and inference framework overhead can increase those requirements further.

Speed also depends on hardware and configuration. In Hugging Face tests, a single high-memory M3 Ultra ran some quantized versions at around 15 tokens per second, while a server with eight H200 GPUs ran the FP8 version at around 65 tokens per second. For small-scale or occasional use, calling the API directly is usually more practical. Organizations with sustained, high-volume usage that prioritize control over their data can consider self-hosting, but there’s no universal usage threshold that guarantees it’ll be cheaper.

Impact on Businesses and the Industry

Mistral emphasizes European infrastructure, private deployment, model customization, and business continuity. These capabilities aren’t unique to Mistral—Chinese open models can also be deployed privately. The real differences come down to deployment location, legal environment, contractual support, and an organization’s requirements for trusting its vendor.

Mistral’s MaaS restrictions mainly target businesses that provide third parties with access to general-purpose model inference or post-training. Separate provisions apply to cases such as end products with embedded features, simple request forwarding, and internal enterprise use. Businesses should check the full license against their specific use case.

Cybersecurity is another area to watch. ENISA is testing Large 4 for vulnerability identification, code review, and log analysis, while organizations including Booz Allen Hamilton have published positive evaluations. These are partner statements, though—they don’t mean the model has been fully deployed or independently verified. The 96.6% malicious-request refusal rate reported by Mistral is also based only on its selected test prompts; it doesn’t mean the model can block the same proportion of real-world attacks.

Frequently Asked Questions

Mistral Large 4: Key Takeaways

• Europe’s strongest, with a caveat: Its score of 26 is the highest outside China, but just one point ahead of Muse Spark Open.
• Overall capabilities still lag: It trails DeepSeek and GLM by 6 index points, with both scoring 32.
• Coding agent performance is close: Its score of 48 is just one point below GLM-5.3 and ahead of DeepSeek’s 46.
• Token efficiency stands out: It uses roughly 8.5 million tokens per task, about half as many as GLM and DeepSeek.
• Cost depends on task results: The evaluation cost was about $2.06 per task, lower than GLM but higher than DeepSeek.
• Open doesn’t mean unrestricted: MaaS businesses that meet the specified definition should check the additional commercial licensing requirements.
• Start with a small-scale test: Compare candidate models on real tasks through APIYI apiyi.com.

Conclusion

Returning to the question in the title, how far Mistral Large 4 is from DeepSeek and GLM depends on what you’re comparing:

1. Overall capabilities: It still trails by 6 points and hasn’t overtaken them in the global rankings.
2. Coding agents: Its score is very close, and it uses significantly fewer tokens on the corresponding test suite.
3. Cost and deployment: It sits between DeepSeek and GLM, while offering distinct options such as European infrastructure and enterprise customization.

This release matters not just because there’s another trillion-parameter model, but because teams with coding, security, and self-managed deployment needs have another competitive option. Whether it’s worth migrating depends on real-world task performance, the total cost of completing tasks, and deployment requirements.

We recommend using the free test credits at APIYI apiyi.com to run a small-scale comparison first, then deciding on your next steps based on the results.

📚 References

1. Mistral official announcement: Improved model weights are now publicly available
2. Link: mistral.ai/news/mistral-large-4-oss

3. Details: Release information, official evaluations, and partner statements

4. Mistral official pricing documentation: API and regional inference pricing

5. Link: docs.mistral.ai/inference/pricing

6. Details: Standard, cached, and service-specific pricing

7. Hugging Face model page and license: Large 4 weights and the MEOW License

8. Link: huggingface.co/mistralai/Mistral-Large-4-1T-A52B

9. Details: Model card and access to the full license

10. Hugging Face technical overview: Architecture and deployment

11. Link: huggingface.co/blog/mistral-large4

12. Details: MoE architecture, inference frameworks, and hardware test results

13. Artificial Analysis open model leaderboard: Independent evaluation data

14. Link: artificialanalysis.ai/models/open-source

15. Details: Overall index, coding agent, cost, and speed data

16. Unsloth deployment guide: Quantization and local inference

17. Link: unsloth.ai/docs/models/mistral-large-4

18. Details: Estimated memory requirements at different quantization levels

19. DeepSeek official pricing: Peak and off-peak API pricing

20. Link: api-docs.deepseek.com/quick_start/pricing

21. Details: Pricing and time periods for DeepSeek V4.1 Flash 1017

22. GLM-5.3 license: MaaS security review terms

23. Link: huggingface.co/zai-org/GLM-5.3/blob/main/LICENSE

24. Details: Revenue thresholds and commercial use requirements

25. VentureBeat report: Large 4 rankings and cost analysis

26. Link: venturebeat.com/technology/mistral-large-4-le-chonk-arrives-as-the-top-open-model-outside-china-with-improved-coding-performance-and-63-lower-cost-per-task

27. Details: Model rankings, token efficiency, and changes in cost per task

28. Hacker News discussion: Early developer feedback

    • Link: news.ycombinator.com/item?id=50289418
    • Details: Perspectives on pricing, self-hosting, and license disputes; comments reflect individual experiences only

Author: APIYI Technical Team
Technical inquiries: Contact us through the APIYI homepage. For more resources, visit the documentation center at docs.apiyi.com.

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