Gemini 2.0 Flash vs Llama 4 Maverick 17B Instruct vs Claude Sonnet 3.5 v2
Gemini 2.0 Flash comes out ahead, 65 to 55 and 54 on our weighted score.
- Our pick
GoogleGemini 2.0 Flash
65/100- ECI134.7
- Price—
- Context1.05M
MetaLlama 4 Maverick 17B Instruct
55/100- ECI132.2
- Price$0.321 / $0.91
- Context1M
AnthropicClaude Sonnet 3.5 v2
54/100- ECI133.5
- Price$3.00 / $15.00
- Context200K
Gemini 2.0 Flash is our pick
Gemini 2.0 Flash is the better all-round choice, scoring 65/100 against Llama 4 Maverick 17B Instruct (55) and Claude Sonnet 3.5 v2 (54). It leads on inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityGemini 2.0 FlashCapabilities Index (ECI): Gemini 2.0 Flash 134.7 · Claude Sonnet 3.5 v2 133.5 · Llama 4 Maverick 17B Instruct 132.2
- Lowest priceLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct $0.468 · Claude Sonnet 3.5 v2 $6.00 per 1M tokens (3:1 blend) · Gemini 2.0 Flash unpriced
- Longest contextGemini 2.0 FlashGemini 2.0 Flash 1,048,576 · Llama 4 Maverick 17B Instruct 1,000,000 · Claude Sonnet 3.5 v2 200,000 tokens
- Widest inputsGemini 2.0 FlashGemini 2.0 Flash: Text, Images, PDFs, Audio, Video · Llama 4 Maverick 17B Instruct: Text, Images · Claude Sonnet 3.5 v2: Text, Images, PDFs
- Self-hostingLlama 4 Maverick 17B InstructPublishes downloadable weights
| Measure | Weight | Gemini 2.0 Flash | Llama 4 Maverick 17B Instruct | Claude Sonnet 3.5 v2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 59 | 56 | 57 |
| Inputs & features | 20% | 90 | 50 | 60 |
| Context window | 13% | 61 | 60 | 32 |
| Overall | 100% | 65/100 | 55/100 | 54/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.0 FlashGoogle | Llama 4 Maverick 17B InstructMeta | Claude Sonnet 3.5 v2Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 134.7 (best) | 132.2 | 133.5 |
| ECI rank | #116 of 148 (best) | #122 of 148 | #119 of 148 |
| GPQA DiamondGraduate-level science questions | — | 67.0% (best) | 55.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 20.6% (best) | 8.5% |
| Price per million tokens | |||
| Input | — | $0.321 (best) | $3.00 |
| Output | — | $0.91 (best) | $15.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.468 (best) | $6.00 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 6 providers | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 1,000,000 tokens | 200,000 tokens |
| Max output | 8,192 tokens | 16,384 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | No | Yes |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | — | — |
| API providers | — | 6 (best) | 1 |
| Released | Dec 11, 2024 | Apr 5, 2025 | Oct 22, 2024 |
| Knowledge cutoff | Jun 2024 | Aug 2024 | Apr 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.0 Flash—
- Llama 4 Maverick 17B Instruct$5.03
- Claude Sonnet 3.5 v2$60.00
Which should you choose?
Which is better: Gemini 2.0 Flash, Llama 4 Maverick 17B Instruct or Claude Sonnet 3.5 v2?
Gemini 2.0 Flash is the better all-round choice, scoring 65/100 against Llama 4 Maverick 17B Instruct (55) and Claude Sonnet 3.5 v2 (54). It leads on inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Gemini 2.0 Flash, Llama 4 Maverick 17B Instruct or Claude Sonnet 3.5 v2?
Llama 4 Maverick 17B Instruct is cheaper at $0.321 input / $0.91 output per million tokens (median across 6 API providers). Claude Sonnet 3.5 v2 costs $3.00 input / $15.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.468 per million tokens for Llama 4 Maverick 17B Instruct versus $6.00 for Claude Sonnet 3.5 v2 (13× as much). Gemini 2.0 Flash has no published per-token price.
Which scores higher on benchmarks?
Gemini 2.0 Flash scores higher on the Capabilities Index (ECI): Gemini 2.0 Flash 134.7 (#116 of 148), Claude Sonnet 3.5 v2 133.5 (#119 of 148) and Llama 4 Maverick 17B Instruct 132.2 (#122 of 148). The confidence ranges of the top two overlap (124.3–136.7 vs 129.2–137.5), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.0 Flash, Llama 4 Maverick 17B Instruct and Claude Sonnet 3.5 v2 yet, so there is no like-for-like coding score. On overall capability, Gemini 2.0 Flash 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?
Gemini 2.0 Flash has the largest context window at 1,048,576 tokens, against 1,000,000 for Llama 4 Maverick 17B Instruct and 200,000 for Claude Sonnet 3.5 v2. Maximum output per response: Gemini 2.0 Flash up to 8,192, Llama 4 Maverick 17B Instruct up to 16,384, Claude Sonnet 3.5 v2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.0 Flash accepts text, images, PDFs, audio and video; Llama 4 Maverick 17B Instruct accepts text and images; Claude Sonnet 3.5 v2 accepts text, images and PDFs. Gemini 2.0 Flash handles the widest range of inputs.
Are any of these open source?
Llama 4 Maverick 17B Instruct publishes its weights and can be self-hosted; Gemini 2.0 Flash and Claude Sonnet 3.5 v2 is proprietary.
Which is newer?
Llama 4 Maverick 17B Instruct is the newest, released Apr 5, 2025. Gemini 2.0 Flash came out Dec 11, 2024; Claude Sonnet 3.5 v2 came out Oct 22, 2024. Knowledge cutoff: Gemini 2.0 Flash Jun 2024, Llama 4 Maverick 17B Instruct Aug 2024, Claude Sonnet 3.5 v2 Apr 30, 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 Oct 4, 2026.
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