Claude Haiku 4.5 vs MiniMax-M2 vs Qwen3 Max
Too close to call on our weighted score (MiniMax-M2 48, Claude Haiku 4.5 48, Qwen3 Max 31). The right pick depends on what you value most.
AnthropicClaude Haiku 4.5
48/100- ECI142.4
- Price$1.00 / $5.00
- Context200K
MiniMaxMiniMax-M2
48/100- ECI—
- Price$0.30 / $1.20
- Context205K
Alibaba (Qwen)Qwen3 Max
31/100- ECI142.4
- Price$1.20 / $6.00
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (MiniMax-M2 48/100, Claude Haiku 4.5 48/100, Qwen3 Max 31/100), so choose by what matters most for your work: MiniMax-M2 on price and Qwen3 Max for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMiniMax-M2MiniMax-M2 $0.525 · Claude Haiku 4.5 $2.00 · Qwen3 Max $2.40 per 1M tokens (3:1 blend)
- Longest contextQwen3 MaxQwen3 Max 262,144 · MiniMax-M2 204,800 · Claude Haiku 4.5 200,000 tokens
- Widest inputsClaude Haiku 4.5Claude Haiku 4.5: Text, Images, PDFs · MiniMax-M2: Text · Qwen3 Max: Text
- Self-hostingMiniMax-M2Publishes downloadable weights
| Measure | Weight | Claude Haiku 4.5 | MiniMax-M2 | Qwen3 Max |
|---|---|---|---|---|
| Price | 50% | 36 | 63 | 32 |
| Inputs & features | 30% | 80 | 35 | 25 |
| Context window | 20% | 32 | 32 | 37 |
| Overall | 100% | 48/100 | 48/100 | 31/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Haiku 4.5Anthropic | MiniMax-M2MiniMax | Qwen3 MaxAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 142.4 (best) | — | 142.4 |
| ECI rank | #90 of 148 (best) | — | #91 of 148 |
| GPQA DiamondGraduate-level science questions | 71.2% | — | 72.6% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 19.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.7% | — | 73.3% (best) |
| SimpleQA VerifiedShort factual questions | 13.2% | — | 48.8% (best) |
| Price per million tokens | |||
| Input | $1.00 | $0.30 (best) | $1.20 |
| Output | $5.00 | $1.20 (best) | $6.00 |
| Cached input | $0.10 | — | — |
| Blended (3:1) | $2.00 | $0.525 (best) | $2.40 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Anthropic API | Official MiniMax (minimax.io) API | Official Alibaba API |
| Limits | |||
| Context window | 200,000 tokens | 204,800 tokens | 262,144 tokens (best) |
| Max output | 64,000 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | claude-haiku-4-5 | MiniMax-M2 | qwen3-max |
| API providers | 34 (best) | 13 | 16 |
| Released | Oct 15, 2025 | Oct 27, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Feb 28, 2025 | — | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Claude Haiku 4.5$20.00
- MiniMax-M2$5.40
- Qwen3 Max$24.00
Which should you choose?
Which is better: Claude Haiku 4.5, MiniMax-M2 or Qwen3 Max?
It is close. Our weighted score puts them within a point (MiniMax-M2 48/100, Claude Haiku 4.5 48/100, Qwen3 Max 31/100), so choose by what matters most for your work: MiniMax-M2 on price and Qwen3 Max for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Claude Haiku 4.5, MiniMax-M2 or Qwen3 Max?
MiniMax-M2 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Claude Haiku 4.5 costs $1.00 input / $5.00 output per million tokens (official Anthropic API price); Qwen3 Max costs $1.20 input / $6.00 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M2 versus $2.00 for Claude Haiku 4.5 (3.8× as much) and $2.40 for Qwen3 Max (4.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Claude Haiku 4.5 has an ECI of 142.4, MiniMax-M2 has not been scored yet and Qwen3 Max has an ECI of 142.4.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Haiku 4.5, MiniMax-M2 and Qwen3 Max yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 Max has the largest context window at 262,144 tokens, against 204,800 for MiniMax-M2 and 200,000 for Claude Haiku 4.5. Maximum output per response: Claude Haiku 4.5 up to 64,000, MiniMax-M2 up to 131,072, Qwen3 Max up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Claude Haiku 4.5 accepts text, images and PDFs; MiniMax-M2 accepts text; Qwen3 Max accepts text. Claude Haiku 4.5 handles the widest range of inputs.
Are any of these open source?
MiniMax-M2 publishes its weights and can be self-hosted; Claude Haiku 4.5 and Qwen3 Max is proprietary.
Which is newer?
MiniMax-M2 is the newest, released Oct 27, 2025. Claude Haiku 4.5 came out Oct 15, 2025; Qwen3 Max came out Sep 23, 2025. Knowledge cutoff: Claude Haiku 4.5 Feb 28, 2025, Qwen3 Max Apr 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
