Every figure this video puts on screen, chased to a primary source.
Antigma’s own measurement page for Ante.
Source: Antigma Labs, “Resource Footprint”, Ante documentation. https://docs.antigma.ai/benchmarks/compare_table
Methodology, in Antigma’s words: “we ran the same 20 tasks in parallel through Ante, Claude Code, and Opencode, each inside Docker with identical constraints, and recorded CPU, memory, and disk usage throughout the run.”
| Agent | Wall time |
|---|---|
| Ante | 940 |
| Claude Code | 627 |
| Opencode | 1076 |
| Agent | Peak | Avg | P95 | P99 |
|---|---|---|---|---|
| Ante | 94.4 | 1.3 | 6.2 | 12.3 |
| Claude Code | 89.5 | 12.1 | 31.0 | 43.4 |
| Opencode | 90.8 | 3.8 | 27.1 | 62.3 |
Average CPU ratio, Claude Code against Ante: 12.1 / 1.3 = 9.3x. The video says “roughly nine times higher”.
| Agent | Peak | Avg | P95 | P99 |
|---|---|---|---|---|
| Ante | 1968 | 683 | 1489 | 1550 |
| Claude Code | 13877 | 3685 | 8927 | 9535 |
| Opencode | 12944 | 2077 | 11266 | 12852 |
Peak memory ratio, Claude Code against Ante: 13877 / 1968 = 7.05x. The video says “roughly seven times higher”.
| Agent | Peak | Avg | P95 | P99 |
|---|---|---|---|---|
| Ante | 7041 | 3121 | 6975 | 6976 |
| Claude Code | 22467 | 4304 | 10128 | 10193 |
| Opencode | 59689 | 6046 | 29108 | 34744 |
| Agent | Peak | P95 | P99 |
|---|---|---|---|
| Ante | 3.5 | 0.0 | 0.1 |
| Claude Code | 263.9 | 10.4 | 101.9 |
| Opencode | 284.1 | 0.1 | 10.6 |
| Agent | Peak | P95 | P99 |
|---|---|---|---|
| Ante | 186.3 | 3.6 | 61.7 |
| Claude Code | 302.3 | 26.6 | 113.0 |
| Opencode | 302.9 | 14.5 | 296.6 |
| Agent | Total read | Total write |
|---|---|---|
| Ante | 24 | 2785 |
| Claude Code | 17444 | 15116 |
| Opencode | 2224 | 31427 |
Total write ratio, Claude Code against Ante: 15116 / 2785 = 5.4x. That is the row the “five times less disk I/O” headline rests on. Total read ratio is 17444 / 24 = 727x, and 17444 MB is 17.4 GB, which is what the video compares against Ante’s 24 MB.
Source: Antigma Labs, Ante repository README. https://github.com/AntigmaLabs/ante
Grep, git) are embedded in one binary
and one process, and local inference is handled by a managed llama.cpp.”ante --resume <session id>.Source: Antigma Labs, “Philosophy”, Ante documentation. https://docs.antigma.ai/start/philosophy
The stated thesis is “cellular-native” agents: “agents should be lightweight enough to run by the thousands”. “We’re running hundreds and thousands of agent replicas. Each one can’t cost a couple GB of memory.”
The sentence the video calls the most important one in the docs is on the resource page: “Mass parallelism only pays off if a single agent is cheap enough to multiply.”
Source: Antigma Labs, “Interactive TUI”, Ante documentation. https://docs.antigma.ai/usage/tui
“The agent drives interactive programs — dev servers, REPLs, ssh, even other CLI agents
like Claude Code or Codex — in durable named tmux sessions … Sessions live in the
ante- namespace and survive Ante restarts: a later conversation can pick up a running
server where it was left.”
/term <name> attaches or detaches a named session
(https://docs.antigma.ai/usage/slash-commands).
Source: Antigma Labs, “Offline Mode”, Ante documentation. https://docs.antigma.ai/local/offline
Offline mode runs the full agent loop against a local GGUF model through a managed llama.cpp build. “Once a model is on disk, inference needs no network connection at all.”
Source: Antigma Labs, “Verified Models”, Ante documentation. https://docs.antigma.ai/local/verified-models
| Model | Quant | Download | Max context | Status |
|---|---|---|---|---|
| Qwen3.8 27B | Q4_K_M | 17 GB | 256K | Verified |
| MiniMax M2.7 | Q3_K_XL | 102 GB | 192K | Verified |
| Qwen3.5 122B A10B | Q2_K | 45 GB | 256K | Verified |
| Qwen3.6 35B A3B | Q4_K_M | 22 GB | 256K | Verified |
| Qwen3.6 27B | Q4_K_M | 17 GB | 256K | Evaled, 56.2% on Terminal Bench 2.1 |
| GLM 4.7 Flash | Q4_K_M | 18 GB | 198K | Verified |
| Gemma 4 26B A4B | Q4_K_M | 16 GB | 256K | Verified |
| Devstral Small 2 24B | Q4_K_M | 14 GB | 384K | Verified |
| Qwen3.5 9B | Q4_K_M | 5.7 GB | 256K | Verified |
| Gemma 4 E4B | Q4_K_M | 4.7 GB | 128K | Verified |
Antigma defines the two statuses on the same page. Verified means “the model loads, the chat template resolves, and tool calling works in Ante’s agent loop. Not yet through the full eval.” Evaled means it “ran the full public benchmark: same Terminal-Bench 2.1 harness, pinned builds, and auditable Harbor runs”.
The one Evaled row: “Qwen3.6 27B reached 56.2% accuracy across 89 Terminal-Bench 2.1 tasks with 445 trials — a 17 GB download holding its own on the same benchmark we use for frontier models.”
Source: Antigma Labs, “Terminal-Bench”, Ante documentation. https://docs.antigma.ai/benchmarks/eval
“All trials use the same parameters as the official Terminal-Bench leaderboard: 89 tasks, 5 trials per task, strict timeouts and hardware limits.” 89 x 5 = 445 trials.
Ante’s own headline figure at the time of writing is 82.7% on Terminal Bench 2.1 with DeepSeek V4 Flash 0731, across 368 of 445 trials passing.
Source: Qwen, Qwen3.8-27B model card, Hugging Face. https://huggingface.co/Qwen/Qwen3.8-27B
Benchmark rows quoted in the video:
| Benchmark | Qwen3.8 27B | Qwen3.6 27B | Opus 4.6 Max |
|---|---|---|---|
| Terminal Bench 2.1 (Terminus) | 73.0 | 63.4 | 78.2 |
| SWE Bench Pro | 61.7 | 53.5 | 53.4 |
The model card states that SWE Bench Pro figures other than Opus 4.6 Max were produced with the Claude Code harness at temperature 1.0, top_p 0.95, and a 256K context window. That is a different harness from the one Ante’s own 56.2% figure was produced under, which is exactly why the video says the two sets of numbers should not be combined.