Qwen2.5 72B Instruct vs Palmyra X5
Qwen2.5 72B Instruct costs 15.0x less per output token than Palmyra X5 ($0.40 vs $6.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 26.5
- Output $/M
- $0.40
- Coding
- No data
- Overall
- No data
- Output $/M
- $6.00
Qwen2.5 72B Instruct or Palmyra X5: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen2.5 72B Instruct for output tokens 15.0x cheaper ($0.40 vs $6.00 per million).
No benchmark has tested both yet. Qwen2.5 72B Instruct has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and WeirdML; Palmyra X5 has none yet. 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 Qwen2.5 72B Instruct has a AA Intelligence Index result, 7.7.
Directional only
Long documents
Listed context window
Palmyra X5
Palmyra X5 takes 1.04M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 72B Instruct
Qwen2.5 72B Instruct $0.0006, Palmyra X5 $0.0036 per turn.
List prices
At a glance
- Coding index
- Qwen2.5 72B Instruct not benchmarked, Palmyra X5 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
- Qwen2.5 72B Instruct $0.40 against Palmyra X5 $6.00 per million output tokens, 15.0x apart.
- Context window
- Qwen2.5 72B Instruct takes 33K tokens, Palmyra X5 1.04M.
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 Index5 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 27.6% | Not measured | Only Qwen2.5 72B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 4.5% | Not measured | Only Qwen2.5 72B Instruct measured |
| WeirdML | 16.0% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: coding | 1292 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: software and IT | 1292 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | Not measured | Only Qwen2.5 72B Instruct measured |
| Epoch Capabilities Index | 129.0 | Not measured | Only Qwen2.5 72B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 34.5% | Not measured | Only Qwen2.5 72B Instruct measured |
| METR time horizons | 35.8% | Not measured | Only Qwen2.5 72B Instruct measured |
| BALROG | 16.2% | Not measured | Only Qwen2.5 72B Instruct measured |
| TheAgentCompany | 5.7% | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.1% | Not measured | Only Qwen2.5 72B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | Not measured | Only Qwen2.5 72B Instruct measured |
| GPQA Diamond (Epoch AI run) | 49.1% | Not measured | Only Qwen2.5 72B Instruct measured |
| BIG-Bench Hard | 79.8% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts | 1271 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: expert | 1245 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts in English | 1279 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| AA Math Index | 14.0 | Not measured | Only Qwen2.5 72B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 14.0% | Not measured | Only Qwen2.5 72B Instruct measured |
| OTIS Mock AIME | 8.1% | Not measured | Only Qwen2.5 72B Instruct measured |
| MATH level 5 | 63.2% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: math | 1283 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: mathematical work | 1290 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 72.0% | Not measured | Only Qwen2.5 72B Instruct measured |
| MMLU | 85.3% | Not measured | Only Qwen2.5 72B Instruct measured |
| AI2 Reasoning Challenge | 94.5% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: science | 1275 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: medicine and healthcare | 1258 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: legal and government | 1288 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: business and finance | 1253 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 11 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards measured
| Benchmark | Qwen2.5 72B Instruct | Palmyra X5 | Leader |
|---|---|---|---|
| IFBench | 36.9% | Not measured | Only Qwen2.5 72B Instruct measured |
| DTBench | 62.9% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMCA | 13.4% | Not measured | Only Qwen2.5 72B Instruct measured |
| HellaSwag | 84.8% | Not measured | Only Qwen2.5 72B Instruct measured |
| WinoGrande | 82.3% | Not measured | Only Qwen2.5 72B Instruct measured |
| PIQA | 82.6% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text | 1269 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: instruction following | 1254 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: creative writing | 1221 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: English | 1283 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: non-English | 1252 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Chinese | 1273 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: German | 1234 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Japanese | 1180 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Korean | 1188 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: French | 1280 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Spanish | 1255 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Russian | 1264 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: multi-turn | 1272 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: longer queries | 1281 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: writing and language | 1247 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: entertainment and media | 1211 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 24 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
Qwen2.5 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 72B Instruct 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 | Qwen2.5 72B Instruct | Writer Palmyra X5 |
|---|---|---|
| Maker | Qwen | Writer |
| Weights | Open weights | No public weights listed |
| Released | September 19, 2024 | January 21, 2026 |
| Context window | 33K | 1.04M |
| Input $/Mtok | $0.36 | $0.60 |
| Output $/Mtok | $0.40 | $6.00 |
| 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 Qwen2.5 72B Instruct better than Palmyra X5 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen2.5 72B Instruct for output tokens 15.0x cheaper ($0.40 vs $6.00 per million).
Which is cheaper, Qwen2.5 72B Instruct or Palmyra X5?
Qwen2.5 72B Instruct costs $0.36 per million input tokens and $0.40 per million output tokens; Palmyra X5 costs $0.60 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen2.5 72B Instruct or Palmyra X5?
Palmyra X5, with 1.04M tokens against 33K.
Has any benchmark tested both Qwen2.5 72B Instruct and Palmyra X5?
No benchmark has tested both yet. Qwen2.5 72B Instruct has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and WeirdML; Palmyra X5 has none yet. Compare them on price and context instead. Data as of October 22, 2026.