Nex-N2.5-Mini vs o1
Nex-N2.5-Mini costs 600.0x less per output token than o1 ($0.10 vs $60.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.10
- Coding
- No data
- Overall
- 44.6
- Output $/M
- $60.00
Nex-N2.5-Mini or o1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nex-N2.5-Mini for output tokens 600.0x cheaper ($0.10 vs $60.00 per million) and a larger context window (262K against 200K tokens).
No benchmark has tested both yet. Nex-N2.5-Mini has none yet; o1 on Aider polyglot, AA Coding Index and LiveCodeBench (Vals AI). Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only o1 has a AA Intelligence Index result, 15.2.
Directional only
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Long documents
Listed context window
Nex-N2.5-Mini
Nex-N2.5-Mini takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nex-N2.5-Mini
Nex-N2.5-Mini $0.0001, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- Nex-N2.5-Mini not benchmarked, o1 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Nex-N2.5-Mini $0.10 against o1 $60.00 per million output tokens, 600.0x apart.
- Context window
- Nex-N2.5-Mini takes 262K tokens, o1 200K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| AA Coding Index | Not measured | 39.7 | Only o1 measured |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 67.9% | Only o1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 12.9% | Only o1 measured |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| LMArena Text: coding | Not measured | 1367 | Only o1 measured |
| LMArena Text: software and IT | Not measured | 1366 | Only o1 measured |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 15.2 | Only o1 measured |
| Epoch Capabilities Index | Not measured | 141.7 | Only o1 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 62.6% | Only o1 measured |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 73.2% | Only o1 measured |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 74.7% | Only o1 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 7.0% | Only o1 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 76.8% | Only o1 measured |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | Not measured | 1371 | Only o1 measured |
| LMArena Text: expert | Not measured | 1361 | Only o1 measured |
| LMArena Text: hard prompts in English | Not measured | 1372 | Only o1 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | Not measured | 73.3% | Only o1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | Not measured | 1388 | Only o1 measured |
| LMArena Text: mathematical work | Not measured | 1390 | Only o1 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | Not measured | 80.4% | Only o1 measured |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.1% | Only o1 measured |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | Not measured | 1369 | Only o1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1355 | Only o1 measured |
| LMArena Text: legal and government | Not measured | 1368 | Only o1 measured |
| LMArena Text: business and finance | Not measured | 1328 | Only o1 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index25 boards measured
| Benchmark | Nex-N2.5-Mini | o1 | Leader |
|---|---|---|---|
| IFBench | Not measured | 70.3% | Only o1 measured |
| Long context reasoning (AA-LCR) | Not measured | 65.0% | Only o1 measured |
| DTBench | Not measured | 74.7% | Only o1 measured |
| LMCA | Not measured | 22.3% | Only o1 measured |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| LMArena Text | Not measured | 1366 | Only o1 measured |
| LMArena Text: instruction following | Not measured | 1367 | Only o1 measured |
| LMArena Text: creative writing | Not measured | 1347 | Only o1 measured |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | Not measured | 1372 | Only o1 measured |
| LMArena Text: non-English | Not measured | 1358 | Only o1 measured |
| LMArena Text: Chinese | Not measured | 1394 | Only o1 measured |
| LMArena Text: German | Not measured | 1337 | Only o1 measured |
| LMArena Text: Japanese | Not measured | 1346 | Only o1 measured |
| LMArena Text: Korean | Not measured | 1396 | Only o1 measured |
| LMArena Text: French | Not measured | 1344 | Only o1 measured |
| LMArena Text: Spanish | Not measured | 1345 | Only o1 measured |
| LMArena Text: Russian | Not measured | 1356 | Only o1 measured |
| LMArena Text: multi-turn | Not measured | 1355 | Only o1 measured |
| LMArena Text: longer queries | Not measured | 1378 | Only o1 measured |
| LMArena Text: writing and language | Not measured | 1360 | Only o1 measured |
| LMArena Text: entertainment and media | Not measured | 1358 | Only o1 measured |
| LMArena Vision: English | Not measured | 1162 | Only o1 measured |
| LMArena Vision: Chinese | Not measured | 1235 | Only o1 measured |
| Neither model has a result on the other 21 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Nex-N2.5-Mini costs less
Repository review
50K input + 3K output tokens
Nex-N2.5-Mini costs less
Agent loop
200K input + 10K output tokens
Nex-N2.5-Mini costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Nex AGI Nex-N2.5-Mini | OpenAI o1 |
|---|---|---|
| Maker | Nex AGI | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 8, 2026 | December 17, 2024 |
| Context window | 262K | 200K |
| Input $/Mtok | $0.025 | $15.00 |
| Output $/Mtok | $0.10 | $60.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nex-N2.5-Mini better than o1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nex-N2.5-Mini for output tokens 600.0x cheaper ($0.10 vs $60.00 per million) and a larger context window (262K against 200K tokens).
Which is cheaper, Nex-N2.5-Mini or o1?
Nex-N2.5-Mini costs $0.025 per million input tokens and $0.10 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Nex-N2.5-Mini or o1?
Nex-N2.5-Mini, with 262K tokens against 200K.
Has any benchmark tested both Nex-N2.5-Mini and o1?
No benchmark has tested both yet. Nex-N2.5-Mini has none yet; o1 on Aider polyglot, AA Coding Index and LiveCodeBench (Vals AI). Compare them on price and context instead. Data as of October 21, 2026.