Grok 3’s 2025 Launch: Benchmarks, Reasoning and DeepSearch

Matthew Berman reviews xAI’s launch charts, a historical user-preference leaderboard and an early product demonstration.

EMElon Muskゲスト
チャンネル
Matthew Berman
公開日
2025年2月18日
動画
13:24
読了時間
3 分
スライド
4
原文
英語
YouTube で開く
スクロールすると動画は右下に小さくなって再生を続けます。字幕は閲覧言語に合わせて切り替わります。
閲覧言語1 言語で読めます
この記事はまだ日本語に翻訳されていないため、英語の原文を表示しています。
要約

30 秒でわかる

In February 2025, Berman discusses Grok 3, xAI’s benchmark comparisons, its reasoning interface and a DeepSearch demonstration. He highlights rapid development while noting that an alternative chart adds a stronger o3 comparison; the results and rankings shown reflect the launch period.

  1. 1Vendor benchmarksThe launch chart compares selected contemporary models on math, science and coding.
  2. 2User-preference rankingBerman discusses a historical LM Arena snapshot under the name “chocolate.”
  3. 3Missing comparisonAn edited chart adds o3 results that were not in the original xAI comparison.
  4. 4Partial reasoning traceMusk says some of the displayed thinking is obscured.
  5. 5DeepSearch demonstrationAn xAI presenter shows searches and source summaries for a launch question.

ポイント

  1. xAI reported strong results on selected math, science and coding benchmarks at launch.
  2. A user-preference leaderboard and vendor benchmarks measure different things and change over time.
  3. A chart that includes additional comparison models can change the apparent ranking.
  4. The displayed reasoning trace is not the entire internal reasoning process, according to Musk.
  5. The early DeepSearch and coding demonstrations do not independently establish accuracy or broad performance.
全文

チャプターごとに整えた全文を読む

文字起こしのみ
01

Launch and initial comparisons

Berman introduces Grok 3 and xAI’s reported results and recalls his predictions.

In February 2025, xAI released Grok 3. I review the launch, xAI’s reported benchmarks and a few early product features. I also note that a model under the name “chocolate” had reached the top of the user-preference LM Arena leaderboard at the time; leaderboard positions change and do not settle every question about model quality.

Before release, I predicted that xAI would reach roughly the level of o1 and might offer a distinctive personality. Based on the initial charts I consider the model competitive, but say I have not yet assessed the personality claims. I also discuss the product’s access to X data as a potential differentiator.

The launch presentation compares Grok 3 and Grok 3 mini with several contemporary non-reasoning models on selected math, science and coding tests. On the chart shown, Grok 3 leads those selected comparisons. These are xAI-reported scores under specific conditions, not independent proof that it beats every model on every task.

xAI’s non-reasoning benchmark chart
スライド 1 / 4xAI’s non-reasoning benchmark chartThe launch chart compares selected models on math, science and coding.

I describe xAI’s account of reinforcement learning focused on math and coding and its claim that the improvements generalized beyond those training domains. A small number of benchmark comparisons can suggest transfer, but do not by themselves establish broad generalization or rule out all contamination.

The team presents AIME 2025 results as a newer math test, with Grok reasoning variants ahead of the models listed on its chart. I regard this as encouraging; it is still a company-presented comparison and should be interpreted with its evaluation setup in mind.

AIME 2025 launch comparison
スライド 2 / 4AIME 2025 launch comparisonThe presentation reports results for reasoning variants on a newer math test.
02

Leaderboard and benchmark context

He discusses user preference, a chart with an added comparison and xAI’s development timeline.

I contrast benchmark scores with LM Arena, where users compare model responses. I note that the “chocolate” entry was identified as an early Grok 3 version and held a leading position in the snapshot I show. Such rankings reflect the users and evaluation window involved.

I then show a modified comparison that adds OpenAI’s December o3 results to an xAI chart that omitted them. On the displayed math and science tests, o3 scores higher. Selecting different comparison models can change the apparent ranking; it does not establish a single best model for all uses.

Comparison including o3
スライド 3 / 4Comparison including o3An amended chart adds an o3 result to the original launch comparison.

I am impressed that xAI reached competitive scores relatively quickly, and review a timeline from Grok 1 to Grok 3. I suggest access to X’s data may have helped; the video does not isolate the effect of that data from compute, training methods or other factors.

03

Compute and early coding test

He reviews claims about the GPU cluster and tries a Snake prompt.

Musk says xAI built a large GPU cluster after providers quoted a longer timeline. I recount his claim of around 100,000 GPUs and his description of building power, cooling and networking capacity. These are statements from the launch, not figures independently verified in this review.

The product interface I show includes research, brainstorming, data analysis, image creation and coding options, plus a Think control. These reflect the interface at the February 2025 launch; features and access may have changed.

The historical Grok interface
スライド 4 / 4The historical Grok interfaceThe interface shows the available controls and feature categories at the time.

I ask Grok to build a Snake game in Python and observe fast text generation, though the displayed process takes around 85 seconds before code output. This is an early demonstration, not a controlled throughput measurement or a complete test of the resulting program.

In a launch clip, Elon Musk says the visible thinking is partially obscured to make immediate model copying harder. The displayed trace therefore should not be treated as a complete view of the model’s internal reasoning.

I discuss the team’s claim that reinforcement learning on math and coding improved other forms of reasoning, and predict ongoing model updates. My broader enthusiasm about reinforcement learning is interpretation, not evidence that one training technique alone explains the model’s performance.

04

DeepSearch and first impressions

A launch clip demonstrates research features, and Berman offers his historical first impressions.

An unnamed xAI presenter demonstrates DeepSearch with a question about a future launch. The interface shows progress, searches and source summaries. The demonstration illustrates the intended workflow, but the video does not independently verify the final answer or demonstrate that every cited source is reliable.

I conclude that the launch has several promising features and say I was unsure whether xAI planned to release the model openly. This is a February 2025 first look, not a statement about later releases, access, leaderboard positions or model capabilities.

同じチャンネルの記事

スライド 6 枚25:50EN
MBMatthew Berman· 2026年10月24日Kimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.Open-weight modelsBenchmarksAI cost & performance+1
スライド 2 枚32:20EN
MBMatthew Berman· 2026年10月23日Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1
スライド0:22EN
MBMatthew Berman· 2026年10月21日A Robot Bodyguard Video Shows Dramatic Rescue ScenariosMatthew Berman · Forward FutureMatthew Berman reacts to a short robot demonstration depicting fighting and vehicle-rescue scenarios.Robotics評価(Evals)
45:08EN
MBMatthew Berman· 2026年10月21日Sam Altman on AI Discovery, Consciousness, Compute and a Human-Centered FutureMatthew Berman, Sam Altman · Forward FutureIn a conversation with Matthew Berman, Altman discusses scientific progress, personal assistants, recursive self-improvement and the importance of human agency.AI for sciencePersonal agentsConsciousness+2
0:32EN
MBMatthew Berman· 2026年10月21日Sam Altman on the Continuing Demand for Software WorkSam Altman, Matthew Berman · OpenAIIn a short interview excerpt, Altman says demand for software may be larger than expected even as AI changes how it is made.Future of WorkSoftware engineeringコーディングエージェント
1:02EN
MBMatthew Berman· 2026年10月15日From Punched Cards to AI Software FactoriesMatthew Berman · Forward FutureMatthew Berman contrasts early programming with an AI-assisted workflow and promotes Factory’s development tools.ソフトウェアファクトリーコーディングエージェントSoftware engineering+1