Agnes 2.5 Pro Theta vs Ling 3.1 Flash
Full profiles: Agnes 2.5 Pro Theta (Sapiens AI) and Ling 3.1 Flash (Ant Group): every benchmark, price and rank.
Updated · Data from 4 sources
As of 9 Nov 2026, Agnes 2.5 Pro Theta and Ling 3.1 Flash are neck and neck on quality (50.8 vs 50.8 on the BenchLeader Index, inside the ±10.9 margin), and Ling 3.1 Flash is 1.3× cheaper at $0.450 vs $0.563 per 1M tokens blended. Ling 3.1 Flash leads in every category we measure both in. Agnes 2.5 Pro Theta streams ~2× faster.
Key facts (as of 9 Nov 2026)
- Agnes 2.5 Pro Theta scores 50.8 on the BenchLeader Index and Ling 3.1 Flash scores 50.8 (BenchLeader, 9 Nov 2026).
- Agnes 2.5 Pro Theta costs $0.563 per 1M tokens blended (list price, 9 Nov 2026).
- Ling 3.1 Flash costs $0.450 per 1M tokens blended (list price, 9 Nov 2026).
- Agnes 2.5 Pro Theta streams ~2× faster than Ling 3.1 Flash (Artificial Analysis, 7-day median, 9 Nov 2026).
- Of the 16 benchmarks both models have, Ling 3.1 Flash leads 14 (BenchLeader, 9 Nov 2026).
Agnes 2.5 Pro Theta benchmarks 
Ling 3.1 Flash benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Either | 46 vs 46 category score; FrontierCode 2.0 (AA) 16.6% vs 17.0% | Either |
| Reasoning | Ling 3.1 Flash | 48 vs 44 category score; HLE-Diamond (AA) 17.2% vs 16.8% | Medium |
| Knowledge | Ling 3.1 Flash | 45 vs 39 category score; AA-Omniscience v1.1 -0.1 vs -11.2 | Medium |
| Long documents | Either | 67 vs 66 category score; AA-LCR v1.1 84.7% vs 83.0% | Either |
| Lowest cost | Agnes 2.5 Pro Theta | $0.150 vs $0.450 per 1M blended; agentic coding session $0.007 vs $0.022 | – |
| Fastest first answer | Agnes 2.5 Pro Theta | 20.9 s (Agnes 2.5 Pro Theta) vs 37.8 s (Ling 3.1 Flash) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Agnes 2.5 Pro Theta and Ling 3.1 Flash are neck and neck on quality (50.8 vs 50.8, within the ±10.9 margin).
- Agnes 2.5 Pro Theta is stronger in long context, multimodal.
- Ling 3.1 Flash is stronger in coding, knowledge, reasoning.
- Ling 3.1 Flash is 1.3× cheaper ($0.450 vs $0.563 per 1M blended).
- Agnes 2.5 Pro Theta streams 2.4× faster (212 vs 90 tokens per second).
At a glance
| Spec | Agnes 2.5 Pro Theta | Ling 3.1 Flash |
|---|---|---|
| Developer | Sapiens AI | Ant Group |
| Released | 7 Nov 2026 | 1 Oct 2026 |
| Context window (tokens) | 1,000,000 | 1,000,000 |
| Compared at | Agnes 2.5 Pro Theta | Ling 3.1 Flash |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $0.100 / $0.300 / – | $0.300 / $0.900 / $0.060 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Agnes 2.5 Pro Theta | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Reasoning | 44.0 | 48.2 | Ling 3.1 Flash |
| Coding | 45.5 | 46.0 | level |
| Knowledge | 38.6 | 44.7 | Ling 3.1 Flash |
| Multimodal | 60.9 | – | not comparable |
| Long context | 67.1 | 66.3 | level |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Agents & tools, Maths, Instruction following, Multimodal, Human preference.
Where sources disagree
Across the 16 benchmarks both have, Agnes 2.5 Pro Theta leads 2, Ling 3.1 Flash leads 14. Every overall index that rates both puts Ling 3.1 Flash ahead.
| Overall index | Agnes 2.5 Pro Theta | Ling 3.1 Flash | Leader |
|---|---|---|---|
| BenchLeader Index | 50.8 | 50.8 | neck and neck |
| AA Intelligence Index v5.0 | 24.0 | 29.6 | Ling 3.1 Flash |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | Agnes 2.5 Pro Theta | Ling 3.1 Flash |
|---|---|---|
| Blended price per 1M tokens | $0.150 | $0.450 |
| Input / output per 1M | $0.100 / $0.300 | $0.300 / $0.900 |
| Cached input per 1M | – | $0.060 |
| Price per task: chat reply | $0.0001 | $0.0004 |
| Price per task: summarize a 30-page report | $0.0014 | $0.0041 |
| Price per task: code edit | $0.0011 | $0.0032 |
| Price per task: agentic coding session | $0.0072 | $0.022 |
| Price per task: structured extraction | $0.0003 | $0.0008 |
| Agentic coding session, input mostly cached | – | $0.011 |
| Cost per benchmark task (AA Index) | $0.575 | $3.19 |
| Output tokens per task (AA Index) | 278k | 225k |
| Of which reasoning | 229k | 151k |
| Time per task (AA Index) | 22 min | – |
| Output speed | 212 tok/s | 90 tok/s |
| Time to first answer | 20.9 s | 37.8 s |
| Time to a complete answer | 29.5 s | 91.0 s |
Per token is not per task: an average task of Artificial Analysis' Intelligence Index cost $0.575 on Agnes 2.5 Pro Theta and $3.19 on Ling 3.1 Flash, and Agnes 2.5 Pro Theta wrote 1.2× as many output tokens for the same work.
Each model is compared at its best-ranked reasoning-effort setting. Agnes 2.5 Pro Theta scores 50.8 and takes 21 s to its first answer token. Ling 3.1 Flash scores 50.8 and takes 38 s to its first answer token. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
26 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Agnes 2.5 Pro Theta | Ling 3.1 Flash |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 50.8 | 50.8 |
| Coding score | BenchLeader | 45.5 | 46.0 |
| Knowledge score | BenchLeader | 38.6 | 44.7 |
| Long context score | BenchLeader | 67.1 | 66.3 |
| Multimodal score | BenchLeader | 60.9 | – |
| Reasoning score | BenchLeader | 44.0 | 48.2 |
| Blended price $/M | BenchLeader | $0.150 | $0.450 |
| Output speed | BenchLeader | 212 tok/s | 90 tok/s |
| Time to first answer | BenchLeader | 20.9 s | 37.8 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,000,000 tokens |
| Reasoning | |||
| Humanity's Last Exam (AA) | Artificial Analysis | 37.2% | 43.5% |
| HLE-Diamond (AA) | Artificial Analysis | 16.8% | 17.2% |
| CritPt | Artificial Analysis | 15.1% | 26.0% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 26.0% | 38.5% |
| Coding | |||
| SciCode (AA) | Artificial Analysis | 56.0% | 56.8% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 10.1% | 33.3% |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 43.5† |
| Design Arena Website | Design Arena | – | 1286 |
| FrontierCode 2.0 (AA) | Artificial Analysis | 16.6% | 17.0% |
| Agents & tools | |||
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 57.3% |
| GDPval-AA v2.1 | Artificial Analysis | 1507 | 1653 |
| AutomationBench | Artificial Analysis | 48.8% | 67.5% |
| GDP.pdf (AA) | Artificial Analysis | 14.2% | 12.6% |
| AA-Briefcase v1.1 | Artificial Analysis | 1496 | 1522 |
| Design Arena Agentic | Design Arena | – | 1200 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 3.8% | 4.3% |
| Knowledge | |||
| AA-Omniscience v1.1 | Artificial Analysis | -11.2 | -0.1 |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 21.3% | 26.5% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 58.6% | 63.7% |
| Multimodal | |||
| MMMU-Pro | Artificial Analysis | 78.4% | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 84.7% | 83.0% |
| MLCR | Artificial Analysis | – | 15.0% |
| Composite | |||
| AA Intelligence Index v5.0 | Artificial Analysis | 24.0 | 29.6 |
| AA Intelligence Index v4.3.2 | Artificial Analysis | – | 43.9 |
| AA Progress Index v1 | Artificial Analysis | – | 321.6 |
Show all 26 benchmarks
| Metric | Publisher | Agnes 2.5 Pro Theta | Ling 3.1 Flash |
|---|---|---|---|
| Metadata | |||
| AA Openness Index | Artificial Analysis | – | 38.9 |
Data as of 2026-11-09. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261103-eaea31df94e6 (https://benchmarks.duellab.org/releases/gb2-20261103-eaea31df94e6/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- Agnes 2.5 Pro Theta: first results.
- Ling 3.1 Flash: 1 score revision.
- Ling 3.1 Flash: BenchLeader Index 50.3 → 50.9.
- Ling 3.1 Flash: 6 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 3 score revisions; Ling 3.1 Flash cost per task moved from $1.01/M to $3.19/M (215% up).
- Ling 3.1 Flash: 1 new result (AA-Briefcase v1.1).
- Ling 3.1 Flash: 1 score revision.
- Ling 3.1 Flash: 1 new result (AA-Briefcase v1.1); 8 score revisions.
- Ling 3.1 Flash: 1 new result (MLCR).
- Ling 3.1 Flash: 2 new results (AutomationBench, GDP.pdf (AA)).
Related comparisons
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Agnes 2.5 Pro Theta vs Ling 3.1 Flash: questions
- Is Agnes 2.5 Pro Theta better than Ling 3.1 Flash?
- Agnes 2.5 Pro Theta and Ling 3.1 Flash are neck and neck on quality (50.8 vs 50.8, within the ±10.9 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 9 Nov 2026, so check the category scores for your use.
- Agnes 2.5 Pro Theta vs Ling 3.1 Flash: which is cheaper, and will that change?
- Ling 3.1 Flash is cheaper today: $0.450 against $0.563 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Agnes 2.5 Pro Theta or Ling 3.1 Flash better for coding?
- They are level in coding (46 vs 46 on the category index). On FrontierCode 2.0 (AA), the top shared coding benchmark, it is 16.6% for Agnes 2.5 Pro Theta against 17.0% for Ling 3.1 Flash.
- Which is faster, Agnes 2.5 Pro Theta or Ling 3.1 Flash?
- Agnes 2.5 Pro Theta streams ~2× faster (212 vs 90 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- They are level on long-context benchmarks (67 vs 66). Both accept 1,000,000 tokens of context.
More questions (3)
- Which has lower latency?
- Agnes 2.5 Pro Theta answers first: 20.9 s against 37.8 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Do independent benchmarks agree on Agnes 2.5 Pro Theta vs Ling 3.1 Flash?
- Across the 16 benchmarks both have, Agnes 2.5 Pro Theta leads 2, Ling 3.1 Flash leads 14. Every overall index that rates both puts Ling 3.1 Flash ahead.
- When was Agnes 2.5 Pro Theta released?
- Agnes 2.5 Pro Theta was released on 7 Nov 2026.