benchmarks: add Artificial Analysis snapshot and placeholder M3.1-Flash estimate

- artificial-analysis/2026-10-13.csv: Anthropic, Qwen, MiniMax-M3 rows with
  cost per task and derived cost per intelligence point
- minimax-m3-1-flash-unverified.md: operator-supplied KingBench figures, unverified
- estimates.csv: TODO placeholder for M3.1 Flash (0.9x M3 cost, index scaled by
  KingBench ratio) -- replace with measured/published values
This commit is contained in:
Paul O'Reilly
2026-10-13 14:33:53 +13:00
parent d7decc1dec
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model,creator,context,intelligence_index,cost_per_task_usd,cost_per_intelligence_point_usd,median_tokens_per_s
Claude Opus 5.5 (max with fallback),Anthropic,1M,58,5.98,0.1031,96
Claude Sonnet 5.5 (max with fallback),Anthropic,1M,56,5.46,0.0975,135
Claude Opus 5.5 (xhigh with fallback),Anthropic,1M,56,3.46,0.0618,81
Claude Opus 5.5 (high with fallback),Anthropic,1M,54,1.82,0.0337,82
Claude Fable 5.1 (max with fallback),Anthropic,1M,53,7.63,0.1440,67
Claude Fable 5.1 (xhigh with fallback),Anthropic,1M,53,5.98,0.1128,62
Claude Sonnet 5.5 (xhigh with fallback),Anthropic,1M,52,2.01,0.0387,103
Claude Opus 5.5 (medium with fallback),Anthropic,1M,51,1.34,0.0263,79
Claude Fable 5.1 (high with fallback),Anthropic,1M,51,3.91,0.0767,52
Claude Fable 5.1 (medium with fallback),Anthropic,1M,49,2.98,0.0608,54
Claude Fable 5.1 (low with fallback),Anthropic,1M,47,2.37,0.0504,56
Claude Sonnet 5.5 (high with fallback),Anthropic,1M,47,0.88,0.0187,96
Claude Haiku 5.5 (max),Anthropic,1M,43,0.21,0.0049,236
Claude Opus 5.5 (low with fallback),Anthropic,1M,42,0.55,0.0131,80
Claude Haiku 5.5 (xhigh),Anthropic,1M,41,0.12,0.0029,187
Claude Sonnet 5.5 (medium with fallback),Anthropic,1M,41,0.48,0.0117,95
Claude Haiku 5.5 (high),Anthropic,1M,38,0.08,0.0021,175
Claude Sonnet 5.5 (low with fallback),Anthropic,1M,36,0.35,0.0097,100
Claude Haiku 5.5 (medium),Anthropic,1M,34,0.05,0.0015,171
Claude Haiku 5.5 (low),Anthropic,1M,29,0.02,0.0007,164
MiniMax-M3,MiniMax,1M,29,0.51,0.0176,107
Qwen3.8 Max (0902),Alibaba,984k,45,5.41,0.1202,36
Qwen3.8 2.4T A95B,Alibaba,262k,40,2.16,0.0540,37
Qwen3.8-Flash-Next,Alibaba,256k,40,0.37,0.0092,51
Qwen3.8 27B (xhigh),Alibaba,256k,34,1.01,0.0297,46
Qwen3.8 27B (medium),Alibaba,256k,28,1.13,0.0404,50
Qwen3.8 27B (low),Alibaba,256k,26,1.05,0.0404,53
Qwen3.7 Plus,Alibaba,1M,25,0.22,0.0088,55
Qwen3.8 27B (non-reasoning),Alibaba,256k,20,2.49,0.1245,52
Qwen3.5 397B A17B,Alibaba,262k,18,0.47,0.0261,88
Qwen3.6 35B A3B,Alibaba,262k,18,0.48,0.0267,129
Qwen3.5 122B A10B,Alibaba,262k,16,0.32,0.0200,129
Qwen3.5 9B,Alibaba,262k,11,0.21,0.0191,58
Qwen3 Coder Next,Alibaba,256k,9,0.78,0.0867,73
1 model creator context intelligence_index cost_per_task_usd cost_per_intelligence_point_usd median_tokens_per_s
2 Claude Opus 5.5 (max with fallback) Anthropic 1M 58 5.98 0.1031 96
3 Claude Sonnet 5.5 (max with fallback) Anthropic 1M 56 5.46 0.0975 135
4 Claude Opus 5.5 (xhigh with fallback) Anthropic 1M 56 3.46 0.0618 81
5 Claude Opus 5.5 (high with fallback) Anthropic 1M 54 1.82 0.0337 82
6 Claude Fable 5.1 (max with fallback) Anthropic 1M 53 7.63 0.1440 67
7 Claude Fable 5.1 (xhigh with fallback) Anthropic 1M 53 5.98 0.1128 62
8 Claude Sonnet 5.5 (xhigh with fallback) Anthropic 1M 52 2.01 0.0387 103
9 Claude Opus 5.5 (medium with fallback) Anthropic 1M 51 1.34 0.0263 79
10 Claude Fable 5.1 (high with fallback) Anthropic 1M 51 3.91 0.0767 52
11 Claude Fable 5.1 (medium with fallback) Anthropic 1M 49 2.98 0.0608 54
12 Claude Fable 5.1 (low with fallback) Anthropic 1M 47 2.37 0.0504 56
13 Claude Sonnet 5.5 (high with fallback) Anthropic 1M 47 0.88 0.0187 96
14 Claude Haiku 5.5 (max) Anthropic 1M 43 0.21 0.0049 236
15 Claude Opus 5.5 (low with fallback) Anthropic 1M 42 0.55 0.0131 80
16 Claude Haiku 5.5 (xhigh) Anthropic 1M 41 0.12 0.0029 187
17 Claude Sonnet 5.5 (medium with fallback) Anthropic 1M 41 0.48 0.0117 95
18 Claude Haiku 5.5 (high) Anthropic 1M 38 0.08 0.0021 175
19 Claude Sonnet 5.5 (low with fallback) Anthropic 1M 36 0.35 0.0097 100
20 Claude Haiku 5.5 (medium) Anthropic 1M 34 0.05 0.0015 171
21 Claude Haiku 5.5 (low) Anthropic 1M 29 0.02 0.0007 164
22 MiniMax-M3 MiniMax 1M 29 0.51 0.0176 107
23 Qwen3.8 Max (0902) Alibaba 984k 45 5.41 0.1202 36
24 Qwen3.8 2.4T A95B Alibaba 262k 40 2.16 0.0540 37
25 Qwen3.8-Flash-Next Alibaba 256k 40 0.37 0.0092 51
26 Qwen3.8 27B (xhigh) Alibaba 256k 34 1.01 0.0297 46
27 Qwen3.8 27B (medium) Alibaba 256k 28 1.13 0.0404 50
28 Qwen3.8 27B (low) Alibaba 256k 26 1.05 0.0404 53
29 Qwen3.7 Plus Alibaba 1M 25 0.22 0.0088 55
30 Qwen3.8 27B (non-reasoning) Alibaba 256k 20 2.49 0.1245 52
31 Qwen3.5 397B A17B Alibaba 262k 18 0.47 0.0261 88
32 Qwen3.6 35B A3B Alibaba 262k 18 0.48 0.0267 129
33 Qwen3.5 122B A10B Alibaba 262k 16 0.32 0.0200 129
34 Qwen3.5 9B Alibaba 262k 11 0.21 0.0191 58
35 Qwen3 Coder Next Alibaba 256k 9 0.78 0.0867 73

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# Artificial Analysis benchmark snapshot
Reference data for the model-registry scores/costs pass. NOT read by any loader
(RegistryEntry ignores unknown fields, so this lives beside the registry, not in it).
- **Source:** https://artificialanalysis.ai/leaderboards/models, extracted from the rendered
table on 2026-10-13 (Anthropic, Alibaba/Qwen and MiniMax rows only; 34 rows with a score and a cost).
- **File:** `2026-10-13.csv` — `intelligence_index` is the AA Intelligence Index; `cost_per_task_usd` is the
site's "Cost per Task (USD)".
- **`cost_per_intelligence_point_usd`** = `cost_per_task_usd / intelligence_index`. Derived here, not a site
column. Chosen as the cost factor to record (user decision, 2026-10-13).
- Rows the site marks with `*` (partial results, no cost shown) are omitted.
- Names are the site's labels. "with fallback" on most Claude rows is the site's wording; effort level is the
bracketed suffix.
- Attribution: data © Artificial Analysis. Re-extract rather than editing by hand.
## Mapping to registry slugs (informational, not authoritative)
| Registry slug | AA row(s) | Note |
|---|---|---|
| `minimax-m3`, `minimax-m3-max` | MiniMax-M3 (index 29, $0.51, 0.0176) | AA lists one M3 row with no effort level; both slugs share it |
| `minimax-m31-flash-{max,medium}` | none (see `../estimates.csv`) | MiniMax-M3.1-Flash-Preview is NOT on the leaderboard yet (checked 2026-10-13; no model page). **TODO(revisit):** `../estimates.csv` holds a placeholder estimate (cost 0.9x M3, index scaled by the KingBench ratio) — replace with measured values |
| `airouter-qwen38`, `airouter-qwen38-max` | Qwen3.8 family | `default_flags.model: "Qwen3.8"` does not say which size; AA has Qwen3.8 27B (low/medium/xhigh), 2.4T A95B, Max (0902), Flash-Next — confirm before mapping |
| `claude-fable-5-1` | Claude Fable 5.1 (low..max) | registry sets `reasoning_effort: low` (inert on the claude runner) |
| `claude-sonnet-5`, `claude-opus-5`, `claude-haiku-4-5` | Sonnet 5.5, Opus 5.5, Haiku 5.5 | slug/model-id names predate the 5.5 releases (`default_flags.model` is `claude-sonnet-5` etc.); verify which API model each actually calls before using these rows |
Cost per task is not comparable to `cost_per_1k_tokens`: it folds in how many tokens the model spends. Use it
for `cost_efficiency` scoring, not for the per-token price fields.

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benchmarks/estimates.csv Normal file
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model,creator,context,intelligence_index,cost_per_task_usd,cost_per_intelligence_point_usd,status,basis
MiniMax-M3.1-Flash-Preview (effort unspecified),MiniMax,1M,61.5,0.46,0.0075,ESTIMATE-TODO,"cost = 0.9 x MiniMax-M3 AA cost per task ($0.51); index = M3 AA index (29) x KingBench-3 ratio (66.25/31.25 = 2.12); both inputs unverified/placeholder"
1 model creator context intelligence_index cost_per_task_usd cost_per_intelligence_point_usd status basis
2 MiniMax-M3.1-Flash-Preview (effort unspecified) MiniMax 1M 61.5 0.46 0.0075 ESTIMATE-TODO cost = 0.9 x MiniMax-M3 AA cost per task ($0.51); index = M3 AA index (29) x KingBench-3 ratio (66.25/31.25 = 2.12); both inputs unverified/placeholder

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# MiniMax-M3.1-Flash-Preview — ad-hoc, UNVERIFIED figures
Supplied by the operator on 2026-10-13 with no source URL. Not from Artificial Analysis (the model is not on
that leaderboard yet — see `artificial-analysis/README.md`). Do not use for registry scores until confirmed.
| Item | Figure | Note |
|---|---|---|
| KingBench 3, M3.1 Flash | 66.25% (53/80) | "community and third-party testing"; arithmetic checks (53/80 = 66.25%) |
| KingBench 3, M3 baseline | 31.25% (25/80) | arithmetic checks; the source also mentions "alternative baseline figures depending on the evaluation set" |
| M3 SWE-bench Pro | ~59% | reference baseline for M3, described as official |
| M3 "Browse.com" | 83.5 | benchmark name looks mistyped (BrowseComp?) — confirm before citing |
| M3 Terminal Bench 2.1 | "strong" | no number given |
Caveats: unknown effort level for the KingBench runs (Flash always thinks; server default is max); KingBench 3 is
small (80 items) so one item is 1.25 points; none of these figures share a scale with the AA Intelligence Index
(M3 = 29 there), so they cannot be mixed into one cost-per-intelligence figure.
## TODO(revisit) — placeholder estimate in use (2026-10-13)
`estimates.csv` carries a stop-gap row for M3.1 Flash: cost per task = 90% of M3's ($0.51 -> $0.46) and
intelligence index = M3's AA index (29) scaled by the KingBench-3 ratio (66.25/31.25 = 2.12) = 61.5. That index
would rank it above Claude Opus 5.5 max (58), which is implausible for a Flash-class model — treat both numbers as
rough placeholders. Replace with (a) the Artificial Analysis row when the model is listed, or (b) our own measured
cost per task at each effort level (fixed task set via `ask-minimax-run --output-format json`).