GPT-4o vs Claude Haiku 3.5 vs Mistral Large 2.1
Claude Haiku 3.5 comes out ahead, 46 to 43 and 38 on our weighted score, and it is the cheaper option too.
OpenAIGPT-4o
43/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
- Our pick
AnthropicClaude Haiku 3.5
46/100- ECI127.0
- Price$0.80 / $4.00
- Context200K
Mistral AIMistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
Claude Haiku 3.5 is our pick
Claude Haiku 3.5 is the better all-round choice, scoring 46/100 against GPT-4o (43) and Mistral Large 2.1 (38). It leads on price and context window. GPT-4o wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-4oCapabilities Index (ECI): GPT-4o 129.0 · Mistral Large 2.1 128.6 · Claude Haiku 3.5 127.0
- Lowest priceClaude Haiku 3.5Claude Haiku 3.5 $1.60 · Mistral Large 2.1 $3.00 · GPT-4o $4.38 per 1M tokens (3:1 blend)
- Longest contextClaude Haiku 3.5Claude Haiku 3.5 200,000 · Mistral Large 2.1 131,072 · GPT-4o 128,000 tokens
- Widest inputsGPT-4o and Claude Haiku 3.5GPT-4o: Text, Images, PDFs · Claude Haiku 3.5: Text, Images, PDFs · Mistral Large 2.1: Text
- Self-hostingMistral Large 2.1Publishes downloadable weights
| Measure | Weight | GPT-4o | Claude Haiku 3.5 | Mistral Large 2.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 51 | 48 | 50 |
| Price | 25% | 19 | 38 | 26 |
| Inputs & features | 15% | 70 | 60 | 25 |
| Context window | 10% | 24 | 32 | 25 |
| Overall | 100% | 43/100 | 46/100 | 38/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-4oOpenAI | Claude Haiku 3.5Anthropic | Mistral Large 2.1Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.0 (best) | 127.0 | 128.6 |
| ECI rank | #141 of 165 (best) | #147 of 165 | #142 of 165 |
| GPQA DiamondGraduate-level science questions | 48.9% | 38.1% | 51.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 6.3% | 4.3% | 7.8% (best) |
| Price per million tokens | |||
| Input | $2.50 | $0.80 (best) | $2.00 |
| Output | $10.00 | $4.00 (best) | $6.00 |
| Cached input | $1.25 | — | — |
| Blended (3:1) | $4.38 | $1.60 (best) | $3.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 3 providers | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 200,000 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens (best) | 8,192 tokens | 16,384 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-4o | — | — |
| API providers | 26 (best) | 4 | 2 |
| Released | May 13, 2024 | Oct 22, 2024 | Nov 18, 2024 |
| Knowledge cutoff | Sep 2023 | Jul 31, 2024 | Nov 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-4o$45.00
- Claude Haiku 3.5$16.00
- Mistral Large 2.1$32.00
Which should you choose?
Which is better: GPT-4o, Claude Haiku 3.5 or Mistral Large 2.1?
Claude Haiku 3.5 is the better all-round choice, scoring 46/100 against GPT-4o (43) and Mistral Large 2.1 (38). It leads on price and context window. GPT-4o wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-4o, Claude Haiku 3.5 or Mistral Large 2.1?
Claude Haiku 3.5 is cheaper at $0.80 input / $4.00 output per million tokens (median across 3 API providers). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers); GPT-4o costs $2.50 input / $10.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $1.60 per million tokens for Claude Haiku 3.5 versus $3.00 for Mistral Large 2.1 (1.9× as much) and $4.38 for GPT-4o (2.7× as much).
Which scores higher on benchmarks?
GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#141 of 165), Mistral Large 2.1 128.6 (#142 of 165) and Claude Haiku 3.5 127.0 (#147 of 165). The confidence ranges of the top two overlap (124.7–131.3 vs 124.3–130.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, GPT-4o 48.9%, Claude Haiku 3.5 38.1%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, GPT-4o 6.3%, Claude Haiku 3.5 4.3%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-4o, Claude Haiku 3.5 and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, GPT-4o leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Claude Haiku 3.5 has the largest context window at 200,000 tokens, against 131,072 for Mistral Large 2.1 and 128,000 for GPT-4o. Maximum output per response: GPT-4o up to 16,384, Claude Haiku 3.5 up to 8,192, Mistral Large 2.1 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
GPT-4o accepts text, images and PDFs; Claude Haiku 3.5 accepts text, images and PDFs; Mistral Large 2.1 accepts text. GPT-4o handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 publishes its weights and can be self-hosted; GPT-4o and Claude Haiku 3.5 is proprietary.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Claude Haiku 3.5 came out Oct 22, 2024; GPT-4o came out May 13, 2024. Knowledge cutoff: GPT-4o Sep 2023, Claude Haiku 3.5 Jul 31, 2024, Mistral Large 2.1 Nov 2024.
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 Nov 2, 2026.
