Pokee-Isaac 28B is INSANE! World’s first 10M Context AI Model Tested
This video tests Pokee Isaac 28B, a 28-billion-parameter model claiming the first real 10-million-token context window, single-GPU deployability, and strong agentic tool-calling, and walks through how to access it, its benchmark comparisons against Gemini, GLM, Nemotron, Qwen, and GPT-5.6, and how it stacks up against DeepSeek for long-horizon agentic work.
Codedigipt8 minTranscript found
Quick learning frame
Read this before watching.
Coding-agent workflow is the loop of inspect, route, plan, edit, verify, summarize, and decide what should be automated next.
New playlist item from Codedigipt; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Skill you build: The ability to evaluate a new long-context AI model's real-world tool-calling and agentic capability by testing it directly in a coding workflow rather than trusting benchmark claims alone.
Watch for the shift from claim to mechanism. The learning value is the point where the transcript reveals a repeatable action, tool boundary, context move, review habit, or artifact.
Concept diagram
Where this video fits.
01Inspect context
02Route tool
03Plan work
04Edit safely
05Verify behavior
06Report next step
Deep lesson
Turn this video into working knowledge.
1,510 cleaned transcript words reviewed across 440 timed caption segments.
Thesis
Pokee-Isaac 28B is INSANE! World’s first 10M Context AI Model Tested teaches a practical coding-agent workflow move: This video tests Pokee Isaac 28B, a 28-billion-parameter model claiming the first real 10-million-token context window, single-GPU deployability, and strong agentic tool-calling, and walks through how to access it, its benchmark comparisons against Gemini, GLM, Nemotron, Qwen, and GPT-5.6, and how it stacks up against DeepSeek for long-horizon agentic work.
The goal is not to remember the video. The goal is to extract the operating principle, tie it to timestamped evidence, test how far the claim transfers, and make something reusable.
0:15
10M context, single GPU
“capability and it can be deployable on a single GPU. And this is the normal that RTX 4090 we use. So they are saying that on this GPU also we can deploy this 10 million token context window...”
Pokee Isaac 28B is presented as the first real 10-million-token context window model, deployable on a single RTX 4090, and was tested inside a coding agent where it correctly flagged issues by severity (critical, high, medium, low) with exact file and line-number references. Run a similar test on any coding model you use: ask it to scan a real file for issues and check whether it returns specific file names and line numbers, not vague descriptions.
2:55
Access and speed claims
“you can use it. Uh and um if you don't know how to use the Jan.ai with the Claude code configuration, then you can visit our channel and here search for this Jan and press enter. And you...”
The model is accessible via console.pokee.ai with a free sign-up credit (about 300 credits, roughly 1-2 credits per chat), pricing of $0.15 per million input tokens and $1 per million output tokens, and a claimed 137k tokens/second peak prefill speed on the ruler long-horizon benchmark, plus integration through Jan.ai mapped to the Sonnet slot in Claude Code. Sign up for the free credits, run the model once in the playground, and time how long it takes to process a long prompt to sanity-check the speed claim yourself.
7:03
DeepSeek comparison caveat
“first model. Uh we have not seen this 10 million uh real model till now, but this is a first model. Now, the question is that whether you should use this model instead of DeepSeek or not. See,...”
The presenter found the GPT-5.6 Luna benchmark comparison confusing because the reported 69.8% score on Terminal Bench 2.1 doesn't match OpenAI's own published low/medium/high scores (49.4%, 58.7%, 73.9%), while the other model comparisons seemed valid; the real differentiator versus DeepSeek is that Pokee Isaac's 10M context (versus DeepSeek's 1M) only matters for long, step-by-step agentic tasks. Before trusting a vendor's benchmark chart, cross-check one contested number against the competing model's own official published score.
01
Inspect context
Start with this video's job: This video tests Pokee Isaac 28B, a 28-billion-parameter model claiming the first real 10-million-token context window, single-GPU deployability, and strong agentic tool-calling, and walks through how to access it, its benchmark comparisons against Gemini, GLM, Nemotron, Qwen, and GPT-5.6, and how it stacks up against DeepSeek for long-horizon agentic work. Treat "Inspect context" as the outcome you are trying to make visible, not a topic label. Anchor it to 0:15, where the video says: “capability and it can be deployable on a single GPU. And this is the normal that RTX 4090 we use. So they are saying that on this GPU also we can deploy this 10 million token context window...”
02
Route tool
Use "Route tool" to locate the part of the coding-agent workflow mechanism the video is demonstrating. Ask what changes in your real setup if this claim is true. Anchor it to 2:55, where the video says: “you can use it. Uh and um if you don't know how to use the Jan.ai with the Claude code configuration, then you can visit our channel and here search for this Jan and press enter. And you...”
03
Plan work
Turn "Plan work" into the reusable artifact for this lesson: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal. This is where watching becomes something you can inspect and reuse.
04
Edit safely
Use "Edit safely" as the application surface. Decide whether the idea touches a browser flow, a local file, a model choice, a source document, a UI, or a review step.
05
Verify behavior
Use "Verify behavior" to prove the lesson. The evidence should connect back to the video title, transcript anchors, and a concrete output, not a generic best-practice claim.
06
Report next step
Use "Report next step" to carry the idea forward: save the prompt, checklist, diagram, or operating rule that would make the next agent run better.
Example
Source-backed artifact packet
Convert the video into a scoped artifact request that includes the transcript claim, mechanism, acceptance criteria, and proof. The output should be a coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal..
Example
Coding-agent workflow proof brief
Separate what the speaker claims, what the demo actually proves, and what still needs outside verification before you adopt the coding-agent workflow pattern.
Example
Teach-back module
Transform the lesson into a definition, a Inspect context -> Route tool -> Plan work -> Edit safely -> Verify behavior -> Report next step diagram, one misconception, one practice exercise, and a check-for-understanding question.
Do not learn it wrong
Treating the title as the lesson without checking what the transcript actually says.
choosing tools by hype
losing context across agents
letting parallel sessions become invisible
Letting the lesson drift into generic Codex vs Claude comparison.
Letting the lesson drift into feature lists without task routing.
Letting the lesson drift into claims that ignore limits or recovery.
Do not count this as learned until these are true.
01
State the transcript-backed claim in your own words: This video tests Pokee Isaac 28B, a 28-billion-parameter model claiming the first real 10-million-token context window, single-GPU deployability, and strong agentic tool-calling, and walks through how to access it, its benchmark comparisons against Gemini, GLM, Nemotron, Qwen, and GPT-5.6, and how it stacks up against DeepSeek for long-horizon agentic work.
02
Explain the practical stakes without hype: New playlist item from Codedigipt; queued for transcript-backed review, topic mapping, and a practical learning artifact.
03
Map the idea onto the Brief -> Source material -> Generation -> Selection -> Edit -> Taste review -> Reusable recipe sequence and name the weakest link.
04
Produce the artifact and include the evidence that proves it: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
Put it into practice
Give this grounded prompt to Codex or Claude after watching.
You are helping me turn one specific YouTube video into real, durable learning.
Source video:
- Title: Pokee-Isaac 28B is INSANE! World’s first 10M Context AI Model Tested
- URL: https://www.youtube.com/watch?v=2pm8ZaZBrqM
- Topic: Creative Automation
- My current learning frame: Pick one long, multi-file coding task you'd normally split into chunks, run it once against Pokee Isaac 28B for the extended context and once against your current model, and compare whether the larger context window actually changes the quality of the agentic result.
- Why this matters: New playlist item from Codedigipt; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Transcript anchors from this exact video:
- 0:15 / Evidence 1: "capability and it can be deployable on a single GPU. And this is the normal that RTX 4090 we use. So they are saying that on this GPU also we can deploy this 10 million token context window..."
- 2:55 / Evidence 2: "you can use it. Uh and um if you don't know how to use the Jan.ai with the Claude code configuration, then you can visit our channel and here search for this Jan and press enter. And you..."
- 4:30 / Evidence 3: "in detail, you can go this link and you can you can actually read it. Okay, now another thing is that you may get confused that whether you should to use this model instead of Deep Seek or..."
- 7:03 / Evidence 4: "first model. Uh we have not seen this 10 million uh real model till now, but this is a first model. Now, the question is that whether you should use this model instead of DeepSeek or not. See,..."
Video-aware target:
- Prompt lane: Coding-agent workflow
- Mechanism to extract: Find the workflow rule that explains when and how to use Codex, Claude Code, browser control, dashboards, or manual review.
- Artifact to produce: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal.
- Artifact must include: task class; agent/tool choice; context packet; verification step; handoff/recovery rule
Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Find the workflow rule that explains when and how to use Codex, Claude Code, browser control, dashboards, or manual review. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal.
5. Include:
- a plain-English definition of the core idea
- a diagram or structured model using this sequence: Inspect context -> Route tool -> Plan work -> Edit safely -> Verify behavior -> Report next step
- answers to these source questions: What workflow pain is being solved? | What exact coordination mechanism is shown? | What evidence proves it changes the work?
- 3 concrete examples that apply the video idea to real agentic work, such as rate-limit routing; browser verification after a UI edit; long-running /goal session review
- 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: choosing tools by hype; losing context across agents; letting parallel sessions become invisible
- a checklist for the next real workflow, focused on: routing decision, context portability, verification, handoff summary
- one practical exercise with a clear done signal: Route three recent tasks across Codex, Claude, browser checks, and manual review with a reason for each.
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.
Quality bar:
- Make this specific to "Pokee-Isaac 28B is INSANE! World’s first 10M Context AI Model Tested", not a generic Creative Automation essay.
- Each workflow rule must point to a timestamped claim or demo moment, then state what remains unproven.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic Codex vs Claude comparison; feature lists without task routing; claims that ignore limits or recovery.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.
Misconceptions
What to stop believing.
Creative AI removes the need for taste.
It increases the need for taste because output volume explodes.
The best prompt is enough.
References, critique, iteration, and post-production matter just as much.
Practice studio
Learning only counts when you make something.
01
Transcript evidence map
Separate what the video actually says from what you already believe about the topic.
3 source-backed takeaways with timestamps, confidence, and a transfer note.02
One useful artifact
Apply the video to a real workflow and produce a coding-agent routing and execution matrix with context needed, tool choice, verification, and done signal..
A reusable artifact with a done signal and one verification step.03
Coding-agent workflow teach-back card
Explain the coding-agent workflow mechanism to someone who has not watched the video yet.
A 90-second explanation, one diagram, one example, and one misconception to avoid.
Recall check
Answer first, then reveal — without rewatching.
What two capabilities made Pokee Isaac 28B notable when tested inside a coding agent?
What pricing and free-credit setup does console.pokee.ai offer for trying the model?
Why did the presenter say the GPT-5.6 Luna benchmark comparison was confusing?
Source shelf
Use the video as a doorway, then verify with primary sources.
