Magnum v4 72B vs GPT-5.2 Pro
Magnum v4 72B costs 33.6x less per output token than GPT-5.2 Pro ($5.00 vs $168.00 per million). Updated October 22, 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
- $168.00
Magnum v4 72B or GPT-5.2 Pro: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Magnum v4 72B for output tokens 33.6x cheaper ($5.00 vs $168.00 per million).
No benchmark has tested both yet. Magnum v4 72B has none yet; GPT-5.2 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. 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
Long documents
Listed context window
GPT-5.2 Pro
GPT-5.2 Pro takes 400K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Magnum v4 72B
Magnum v4 72B $0.0050, GPT-5.2 Pro $0.105 per turn.
List prices
At a glance
- Coding index
- Magnum v4 72B not benchmarked, GPT-5.2 Pro 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-5.2 Pro $168.00 per million output tokens, 33.6x apart.
- Context window
- Magnum v4 72B takes 33K tokens, GPT-5.2 Pro 400K.
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.
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Magnum v4 72B | GPT-5.2 Pro | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 156.3 | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 1 board here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Magnum v4 72B | GPT-5.2 Pro | Leader |
|---|---|---|---|
| ARC-AGI | Not measured | 90.5% | Only GPT-5.2 Pro measured |
| ARC-AGI-2 | Not measured | 54.2% | Only GPT-5.2 Pro measured |
| SimpleBench | Not measured | 57.4% | Only GPT-5.2 Pro measured |
| Chess puzzles | Not measured | 60.0% | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Magnum v4 72B | GPT-5.2 Pro | Leader |
|---|---|---|---|
| FrontierMath, tiers 1 to 3 | Not measured | 74.0% | Only GPT-5.2 Pro measured |
| FrontierMath, tier 4 | Not measured | 46.0% | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Magnum v4 72B | GPT-5.2 Pro | Leader |
|---|---|---|---|
| PRBench Finance | Not measured | 46.3% | Only GPT-5.2 Pro measured |
| PRBench Legal | Not measured | 45.4% | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 18 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
Magnum v4 72B costs less
Repository review
50K input + 3K output tokens
Magnum v4 72B costs less
Agent loop
200K input + 10K output tokens
Magnum v4 72B 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-5.2 Pro |
|---|---|---|
| Maker | Anthracite | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | October 22, 2024 | December 10, 2025 |
| Context window | 33K | 400K |
| Input $/Mtok | $2.50 | $21.00 |
| Output $/Mtok | $5.00 | $168.00 |
| 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 Magnum v4 72B better than GPT-5.2 Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Magnum v4 72B for output tokens 33.6x cheaper ($5.00 vs $168.00 per million).
Which is cheaper, Magnum v4 72B or GPT-5.2 Pro?
Magnum v4 72B costs $2.50 per million input tokens and $5.00 per million output tokens; GPT-5.2 Pro costs $21.00 and $168.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Magnum v4 72B or GPT-5.2 Pro?
GPT-5.2 Pro, with 400K tokens against 33K.
Has any benchmark tested both Magnum v4 72B and GPT-5.2 Pro?
No benchmark has tested both yet. Magnum v4 72B has none yet; GPT-5.2 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. Compare them on price and context instead. Data as of October 22, 2026.