Magnum v4 72B vs GPT-3.5 Turbo
GPT-3.5 Turbo costs 3.3x less per output token than Magnum v4 72B ($1.50 vs $5.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $5.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
Magnum v4 72B or GPT-3.5 Turbo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Magnum v4 72B for a larger context window (33K against 16K tokens).
No benchmark has tested both yet. Magnum v4 72B has none yet; GPT-3.5 Turbo on AA Coding Index, SciCode and AA Intelligence Index. 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 GPT-3.5 Turbo has a AA Intelligence Index result, 5.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
Magnum v4 72B
Magnum v4 72B takes 33K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo
Magnum v4 72B $0.0050, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- Magnum v4 72B not benchmarked, GPT-3.5 Turbo 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
- Magnum v4 72B $5.00 against GPT-3.5 Turbo $1.50 per million output tokens, 3.3x apart.
- Context window
- Magnum v4 72B takes 33K tokens, GPT-3.5 Turbo 16K.
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 Index2 boards measured
| Benchmark | Magnum v4 72B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 10.7 | Only GPT-3.5 Turbo measured |
| SciCode | Not measured | 0.0% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Magnum v4 72B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 5.5 | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Magnum v4 72B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 30.6% | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 29.7% | Only GPT-3.5 Turbo measured |
| BIG-Bench Hard | Not measured | 61.6% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Magnum v4 72B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Magnum v4 72B | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 64.4% | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | Not measured | 58.5% | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 46.2% | Only GPT-3.5 Turbo measured |
| MMLU | Not measured | 68.5% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
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
GPT-3.5 Turbo costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo 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 | Magnum v4 72B | OpenAI GPT-3.5 Turbo |
|---|---|---|
| Maker | Anthracite | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | October 22, 2024 | May 28, 2023 |
| Context window | 33K | 16K |
| Input $/Mtok | $2.50 | $0.50 |
| Output $/Mtok | $5.00 | $1.50 |
| 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 Magnum v4 72B better than GPT-3.5 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Magnum v4 72B for a larger context window (33K against 16K tokens).
Which is cheaper, Magnum v4 72B or GPT-3.5 Turbo?
Magnum v4 72B costs $2.50 per million input tokens and $5.00 per million output tokens; GPT-3.5 Turbo costs $0.50 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Magnum v4 72B or GPT-3.5 Turbo?
Magnum v4 72B, with 33K tokens against 16K.
Has any benchmark tested both Magnum v4 72B and GPT-3.5 Turbo?
No benchmark has tested both yet. Magnum v4 72B has none yet; GPT-3.5 Turbo on AA Coding Index, SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 23, 2026.