HEAD-TO-HEAD · MID-TIER COMPARISON · VERIFIED SEPTEMBER 15, 2026

Which model gives you better value per dollar?

Claude Haiku 4.5 costs half as much per token as Gemini 3.1 Pro across both input and output. Anthropic's budget-tier model runs $1/$5 per million tokens while Google's mid-tier Pro sits at $2/$12. The gap widens on output-heavy workloads where generation cost dominates.

Spec	Claude Haiku 4 5	Gemini 3 1 Pro
Input / 1M tokens	$1.00	$2.00
Output / 1M tokens	$5.00	$12.00
Context window	200K	1M

The output price gap

Output pricing is where the spread becomes undeniable. Haiku 4.5 charges $5 per million output tokens—less than half Gemini 3.1 Pro's $12 rate. For applications that generate long responses, summaries, or code, that 2.4× multiplier compounds quickly. Input is similarly tilted: $1 versus $2 per million means Haiku delivers identical throughput for half the upfront token cost.

Context window

Gemini 3.1 Pro ships a 1-million-token context window, five times larger than Haiku 4.5's 200,000-token capacity. That extra headroom matters for repository-scale codebases, multi-document analysis, or transcripts that exceed 200K. If your prompts rarely push past 100K tokens, the gap is academic; if you routinely work with 500K+ token contexts, Gemini's window becomes a functional requirement regardless of price.

Worked example

At 100 million input and 30 million output tokens per month, Claude Haiku 4.5 costs $100 (input) + $150 (output) = $250 total. Gemini 3.1 Pro costs $200 (input) + $360 (output) = $560 total. Haiku saves $310 monthly—a 55% reduction. The math: Haiku input is 100M × $1/M = $100; output is 30M × $5/M = $150. Gemini input is 100M × $2/M = $200; output is 30M × $12/M = $360.

Prices from the LLM Price Watch daily tracker, September 15, 2026. Prices change; use the calculator with your own usage for an exact comparison, or see the full price table for every tracked model.

Frequently asked questions

Is Claude Haiku 4.5 faster than Gemini 3.1 Pro?

Speed varies by provider and load, but both models typically deliver 90–115 tokens per second in production. Haiku 4.5 is optimized for low-latency responses and often edges ahead on sub-second first-token time. For most applications the throughput difference is negligible; choose on cost and context-window fit rather than raw speed.

Which model performs better on coding benchmarks?

Gemini 3.1 Pro consistently leads on software-engineering and mathematical reasoning benchmarks. Independent comparisons show Gemini scoring in the low-to-mid 90s versus Haiku's low-to-mid 80s on coding tasks. If you need frontier-tier code generation or complex problem-solving, Gemini justifies the premium; for straightforward API calls or extraction, Haiku is sufficient.

Can I switch providers mid-project without rewriting prompts?

Both models support standard OpenAI-compatible APIs via aggregators like OpenRouter, Vercel, and others, making migration straightforward. Prompt tuning may still be necessary—each model has different instruction-following quirks and output formatting. Budget a few hours to validate quality on representative test cases before committing production traffic to either endpoint.

Does the 5× context-window gap matter in practice?

It depends entirely on workload. Most chatbot and RAG pipelines stay well under 50K tokens per request, making Haiku's 200K limit plenty. Long-document analysis, repository-wide code search, or multi-file compilation workflows often exceed 200K and require Gemini's 1M window. Measure your 95th-percentile prompt size before assuming you need the larger context.